Projects

Abstract

ADLAS intends to develop datasets for AI model training in autonomous maritime ships. It is motivated by the need for safe and efficient automated vessel technology. Our research aims to expand AI exploration in autonomous vessels by creating diverse datasets for model testing in simulated environments. Simulators allow for rigorous evaluation of AI model performance and safety under various conditions, which are difficult to build in real life scenarios. The generated datasets will be foundational for future projects and safety standards in the maritime industry. Hence, our research aligns with evolving safety standards and aims to validate AI systems in the field of maritime operations. The outcome will be an open-source dataset which will advance international research collaboration in autonomous maritime ships. Leveraging collaborations and real-world training data, our research aims to enhance AI model adaptability and generalization capabilities. Overall, ADLAS aims to create comprehensive datasets for AI model training in autonomous maritime ships, utilizing AMA's full mission simulators and real-world teaching data to advance safety standards and collaboration in the field.

Researcher(s)

• Promoter: Oramas Mogrovejo José Antonio
• Co-promoter: Anwar Ali

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

We need to reduce energy and increase safety, while at the same time protecting people's privacy. Radio frequency sensing can help, because it allows monitoring presence and estimate activity while not being accurate enough to recognize people directly and device identifiers can be anonymized. Such monitoring traditionally sets specific requirements for network and user equipment. This leads to challenges in deployment and increased e-waste when switching equipment. Our team proposes opportunistic sensing, where all transmitters enable monitoring irrespective of their wireless technology. In this project, we will investigate how to identify transmitters in the environment without prior knowledge of the wireless technology, exploiting for the first time not only specific transmitter characteristics and anomalies, but also usage patterns. While such identification can then support further presence detection and activity estimation, we will also know the extent in which current anonymization strategies are sufficient. Therefore, this project aims to contribute to economic and environmental optimization, and in societal insights. The energy our devices transmit will in the future support not only our communication, but also enable our smart environments, while taking appropriate privacy measures.

Researcher(s)

• Promoter: Berkvens Rafael

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Positioning, Navigation and Timing (PNT) has become increasingly important in enabling many Location Based Services (LBS). Undoubtedly, the most used PNT systems are the Global Navigation Satellite Systems (GNSS), which provide worldwide coverage. Although GNSS has improved significantly over the past decades, multiple shortcomings are inherent to the design of the satellite system. The poor indoor reception and the susceptibility to jamming are a clear illustration of this. Therefore, a novel Low Earth Orbit (LEO) PNT system could provide a solution to these problems. Because these satellites are approximately 20 times closer to Earth, a significant signal strength difference can be observed. However, the topic of indoor localization with LEO satellites remains largely unexplored. To construct a new PNT constellation with good indoor accuracy and coverage the dynamic LEO satellite-to-indoor channel should be taken into account, together with optimizing carrier frequency, modulation, algorithms, and coding techniques within the many constraints. In this work, I quantify the LEO satellite-to-indoor channel characteristics. To achieve this, I will conduct numerous real-world measurements and quantify multipath and signal strength in a variety of indoor environments. This will be achieved by leveraging real LEO-PNT signals provided by ESA. Furthermore, a LEO satellite simulation will be constructed to analyze and optimize the PNT performance for indoor environments.

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Janssen Thomas
• Fellow: Van Uytsel Wout

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Deep learning has achieved remarkable success in solving complex problems across diverse domains. Despite its widespread use, the fundamental concept of generalization to unseen data — which ensures that the model does not memorize (i.e., overfits) the training data but instead learns the underlying features that represent a broader range of examples — remains poorly understood. Generalization performance is commonly assessed post hoc via prediction accuracy on test data. Analyzing generalization without test data, however, unveils the learning process and whether the model is capturing the intended features. This commonly involves evaluating the model complexity, through an analysis of decision boundaries (which delineate different regions of the data space) and the model's learned parameters (which define the mapping of input data to predictions). Current efforts seek to establish generalization bounds or simple metrics correlating with the model's ability to generalize. This project instead aims to exploit topological data analysis, or more precisely persistent homology, to characterize the intrinsic structures within decision boundaries, trained parameters and activations, that contribute to superior generalization. Understanding this relationship holds significant potential for enhancing model design, interpretability and resource efficiency, and providing valuable insights into the behavior and limitations of deep learning, guiding future research directions.

Researcher(s)

• Promoter: Oramas Mogrovejo José Antonio
• Fellow: Turkes Renata

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Extended Reality (XR) continues to gain more traction, with an ever-expanding array of both consumer and business applications. When designing such applications, one can choose between tethered, wirelessly connected or fully standalone Head-Mounted Displays (HMDs). These result in constrained user freedom, compression artifacts, or limited visual fidelity respectively. In this project, we aim to enable a best-of-all-worlds solution, where wireless Millimeter-Wave (mmWave) links are leveraged for consistent extremely high-quality and low-latency data delivery. Current mmWave solutions behave poorly under motion, with link degradation and even interruptions being commonplace. In this project, we will investigate avenues for enabling interactive mobile multi-user XR through mmWave. Consistent room-wide coverage will require multiple Access Points (APs), so we will investigate their optimal placement along with dynamic, proactive assignment of HMDs to APs, including practical zero-latency handovers. Furthermore, we will design real-world proactive receive-side beamforming for HMDs, as to maintain consistently high-quality links during rapid rotational motion. Data rate and reliability will be improved through both current-day solutions such as MU-MIMO, and future solutions such as Reconfigurable Intelligent Surfaces and Distributed Antenna Arrays. All this will be evaluated through a novel testbed along with simulation. Testbed design and simulation tools will be open source.

Researcher(s)

• Promoter: Famaey Jeroen
• Fellow: Struye Jakob

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Greenhouse gas emission is causing the Earth's climate to deteriorate at an alarming rate. Greenhouse gas traps the heat in the atmosphere, causing global warming, extreme weather conditions and environmental changes such as rising sea levels. Agriculture is a significant contributor to greenhouse gas emission, with a contribution of around one-third of total emissions. Considering this, many recent sustainability regulations such as the Flemish Nitrogen Decree and EU Green Deal try to tackle the emission from agriculture. A major portion of agricultural emissions arise from the unscientific use of fertilizers which release greenhouse gases into the atmosphere. As a result, farmers are urged to follow sustainable agriculture practices and reduce emission. However, implementing sustainable farming requires allowing farmers to determine the optimum measurement of both soil nutrient content and emission in real time. But there is a lack of cost-effective devices that can measure both soil nitrogen content and emission in real time and can assist farmers in deciding on optimum fertilizer application. Such a device should be of low cost and with limited maintenance requirements, putting no extra financial pressure on farmers. Moreover, they should be deploy-and-forget architecture so that no maintenance and management is required from the farmers. This POC proposal Sustainable and Energy Neutral Soil Sensing (SENSS) aims to fill this gap by innovating novel soil sensing devices that work seamlessly in agricultural environments. The device will incorporate novel energy harvesting and energy-aware techniques along with low-power wireless connectivity and onboard intelligence to achieve long-term operation and in-device data processing and inference. The project will further leverage the ecology and environmental knowledge of the Global Change Ecology Center of Excellence and the low-power electronic and communication knowledge of IDLab.

Researcher(s)

• Promoter: Singh Ritesh Kumar
• Co-promoter: Famaey Jeroen
• Co-promoter: Janssens Ivan
• Co-promoter: Struyf Eric
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In today's logistics, rail transport progressively shows competitiveness in connecting deep-sea port with the hinterland. As an example, Port of Antwerp-Bruges - the second largest port in Europe – attempts to double the cargo throughput via this modality to 15% by 2030 [1]. Capability to carry large volume, various types of freight, along with high reliability and sustainability are making trains advantageous over traditional trucks. The recent boom of freight railways transport has, however, put more pressure on both inland and deep-sea port infrastructure. In fact, many rail hubs at seaport are still operating sub-optimally due to the incapacity of maximizing their resources utilization. Subsequently, while some bundles are frequently overloaded, others remain rarely used for months. Certain long tracks are blocked to park single wagons during hours, causing shortage when long wagon compositions require service. Train path reservation slot remains fixed for all wagon moving tasks (e.g., 8 hours), resulting in struggles in this resource allocation. These shortcomings are originated from the fact that the rail resource planning is being conducted in a first-come-first-serve, random-pick-up and manual fashion, without having insights of wagon flows in the near future. As a result, even owning large-scale rail resources, some ports still face unworthy shortage or serious delay, which finally adds overheads in total transport cost. Facing this, the optimization of resource allocation based on prediction of up-coming cargo flow will foster the rail infrastructure management, and thus the overall rail operation efficiency. This PhD proposal researches wide range of Machine Learning models to enhance the end-to-end visibility of wagon journey to the deep-sea port. These models forecast the complete path of wagon, from the moments when the long-haul trains are still hours before arrival. The most crucial stages include: arrival time at the main hub, service delay and service time at the shunting yard, train path to move wagon to bundle, public track and time slot to park each wagon, bundling dwelling (such as electric – diesel locomotive shift, which necessitates locomotive and its path allocation), and terminal service (loading/unloading). Next step, the insights learnt from these predictive indicators will be then act as outputs of Optimization phase, to propose the planning of rail tracks, train path, shunting yards and terminal slots which will avoid future shortage, mitigate idle time, and maximize the served cargo volume. Various optimization methods (traditional vs combinatorial neural, single vs multiple objective) will be benchmarked for better accuracy – computational complexity trade-off. The PhD work will be linked with regional (Flanders), national (Belgium) or European research projects, in order to validate the proposed solutions on a real use case with large-scale data. Moreover, it creates synergies upon the solid background of IDLab-imec, which have been showcased through previous projects, including data-driven models, simulation, and optimization aspects, for expanding their contribution in logistics domain.

Researcher(s)

• Promoter: Mercelis Siegfried
• Fellow: Denis Hansi

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The 6th generation of mobile network technology (6G) will be at the centre of a human-centric hyper-connected world. To enable this, 6G will not only have to offer unprecedented throughput, latency, and reliability but also accurate sensing of people and environments. This will enable novel spatially aware applications, such as extended reality, human-robot collaboration, and autonomous transportation. Achieving this vision requires the design of new perceptive radios, that can perform integrated sensing and communications (ISAC). Moreover, the required 6G performance and sensing accuracy can only be achieved by employing mmWave frequencies (i.e., 24-300 GHz). However, commercial-off-the-shelf mmWave devices do not offer the needed flexibility. As such, to go beyond simulation, and enable research into and prototyping of 6G mmWave perceptive radio systems, more flexible hardware, based on software-defined radios and programmable beamformers, is required. The goal of this project is to develop an indoor perceptive radio lab that offers such equipment, to enable the design of novel 6G ISAC solutions for future spatially aware cyber-physical applications. The lab will be equipped with a motion-capture system to collect ground truth data, which can be used to validate sensing accuracy and as input data for training AI algorithms. XR devices will be integrated to enable human-centric experimentation with realistic 6G applications. 1450 / 1500

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Berkvens Rafael
• Co-promoter: Halili Rreze

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Integration of Low Earth Orbit (LEO) space satellites and 5G New Radio (NR) terrestrial communications networks is expected to provide ubiquitous coverage that was never perceived before. This integration will be used to deploy the required communications networks in areas that are very difficult to reach today, thus resulting in new developments in many industries in different countries. In addition, this integration can be a key impact factor to empower a unique Positioning, Navigation, and Timing (PNT) solution required for many services and applications. Given the fact that this research topic is largely unexplored, we aim to conduct a comprehensive investigation and analysis of the physical layer attributes and properties of 5G NR and LEO PNT signals, with an emphasis on their applicability to PNT services. Our analysis will encompass crucial aspects, including received signal strength, channel state information, signal propagation time, and other physical layer characteristics derived from IQ samples of signals received from both LEO PNT satellites and 5G NR base stations at the targeted receiver location. Our objective is to analyze the physical layer features and characteristics of the involved signals and the potential benefits of merging these two technologies to determine whether this fusion can mitigate the limitations associated with each, such as non-line-of-sight propagation when utilizing LEO space segments and 5G NR base stations, high signal losses, and the limited availability of 5G NR base stations in all required areas. The analyses will be performed using Software Defined Radios (SDRs) with the RF front ends able to receive signals from open satellite LEO constellations and open source 5G NR software modules. Further, a processing unit will be used for postprocessing and analyses.

Researcher(s)

• Promoter: Halili Rreze

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Flanders AI Research Program is a strategic basic research program with a consortium of eleven partners: the five Flemish universities (KU Leuven, University of Ghent, University of Antwerp, University of Hasselt, Vrije Universiteit Brussel) and six research centers (imec, Flanders Make, VIB, VITO, Sirris and ILVO). The program brings together 300+ researchers on new AI methods that can be used in innovative applications in health, industry, planet&energy and society. This way, the program contributes to a successful adoption of AI in Flanders. The ambition is for Flanders to occupy a strong international position in the field of strategic basic research in AI, and this within a strong and sustainable Flemish ecosystem. Five focus research themes have been selected: responsible AI, human-centered AI, sustainable AI (energy-efficient and high-performance), productive and data-efficient AI (systems that require little data, which perform by combining data with domain knowledge and experience of experts) and resilient and high-performant AI (robust against changes in the environment). The description of the work packages and their research tasks defines the aspects within these themes that will be investigated in the program. The AI solutions are demonstrated in real-life use cases. These results not only demonstrate the effectiveness, but also inspire companies for adoption and researchers for further research. The Flanders AI Research Program is part of the Flanders AI Policy Plan. More info: www.flandersairesearch.be

Researcher(s)

• Promoter: Mannens Erik
• Co-promoter: Calders Toon
• Co-promoter: Daelemans Walter
• Co-promoter: Famaey Jeroen
• Co-promoter: Goethals Bart
• Co-promoter: Laukens Kris
• Co-promoter: Martens David
• Co-promoter: Mets Kevin
• Co-promoter: Oramas Mogrovejo José Antonio
• Co-promoter: Sijbers Jan
• Co-promoter: Van Leekwijck Werner
• Co-promoter: Verdonck Tim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The overarching objective of 6G-TWIN is to provide the foundation for the design, implementation and validation of an AI-native reference architecture for 6G systems that incorporates Network Digital Twins (NDT) as a core mechanism for the end-to-end, realtime optimisation, management and control of highly dynamic and complex network scenarios. To achieve this objective, 6G-TWIN will deliver methods, modelling and simulation solutions for the definition, creation and management of multi-layered virtual representations of future 6G systems, where heterogeneous domains (i.e., edge, fog and cloud) and communication technologies (e.g., cellular, optical and Non-Terrestrial Networks (NTN)) coexist. The project solutions will be demonstrated in two complementary use cases addressing mobility and energy-efficiency challenges, aligned with the expected use cases of 6G and the Key Performance Indicators (KPI) defined in previously funded projects (including SNS JU STREAM-C/D-2022). Finally, the participation of Small and Medium-sized Enterprises (SMEs) will ensure that the 6G-TWIN consortium pays particular attention to the replication, reengineering and exploitation of the project outcomes, regularly aligning the requirements of standardisation bodies with predicted market needs.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Camelo Botero Miguel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The idea of Joint communication and sensing (JCS) capabilities is a revolutionary and innovative solution. A single system has the potential to offer significant advances in various fields, such as smart transportation, smart cities, smart homes, healthcare, security, and environmental monitoring. iSEE-6G extends beyond JCS and propose a Joint Communication, Computation, Sensing, and Power transfer (JCCSP) unified radio platform, which includes all support elements of the proposed solutions in future 6G networks. By integrating, exploiting, and supporting 6G key enabling technologies, iSEE-6G offers a) JCCSP-oriented novel intelligent reconfigurable surfaces (RIS) and agile beamforming array solutions; b) JCCSP-optimized physical layer design including waveform design, frame structure design, channel modeling, precoding/beamforming with respect to open radio access network (O-RAN) architectural paradigm; c) JCCSP-enabled cross-layer schemes design under new capabilities in terms of service-oriented network architecture; and d) JCCSPimplemented system-level solutions for providing new functionalities towards a cell-free 6G network. The iSEE-6G Proof-of-Concept (PoC) focuses in JCCSP use cases in aerial corridors, where UAVs with various roles providing different services coexist and coordinate with each other. In IMEC's testbed static distributed RUs, and vehicular UEs are additionally included for an emergency response incident. The UAVs monitor the area, estimate and report accurate positioning and provide situational awareness through integrated sensing. In ORO's testbed 5G waveforms based JCCSP exploit the KPI collection capabilities of it. The operation of the testbed will be extended at an outdoor venue, where UAVs and IoT devices will be deployed to test the Wireless Power Transfer (WPT) capabilities. Edge computational power is used for Public Protection and Desaster Relief (PPDR) monitoring and JCCSP-as-a-Service implementation.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Artificial Intelligence (AI) is widely studied and finding increasing adoption across communication technologies spanning network layers and business ecosystems. It is anticipated to play a central role in the design and operation of future 6G networks. Despite the promise of AI, there remain many obstacles to its use in communication networks. The introduction of software defined elements such as radio access network (RAN) intelligent controllers (RIC) enables multi-party applications for the control and management of networks. However, AI functions are still nascent and such structures do not extend to optical networks or multi-controller environments. 6G-XCEL seeks to address these challenges through research on high edge network use cases that employ multi-party AI controls running over compute accelerators to coordinate control across radio and optical networks. It will develop a reference framework for AI in 6G that will pave the way towards global validation, adoption and standardisation of AI approaches. This framework will enable decentralised AI-based network controls across network domains and physical layers, while promoting security and sustainable implementations. Using the latest AI algorithms and data compression, research on the resulting decentralised multi-party, multi-network AI (DMMAI) framework will enable the development of reference use cases, data and model repositories, curated training and evaluation data, as well as technologies for its use as a benchmarking platform for future AI/ML solutions for 6G networks. 6G-XCEL will bring together a large ecosystem of researchers from the EU and US to implement elements of the DMMAI framework in their testbeds and labs, integrating it into their research programs and validating the framework across platforms. Working with standardisation groups within each jurisdiction, 6G-XCEL will achieve joint progress towards large scale application of AI in 6G networks.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Autonomous systems are gaining wider traction in various applications including autonomous vessel navigation, autonomous driving and robotics. This is due to the availability of large amounts of data which is motivating a large body of research into the interpretation of this data and independent decision making on top of it. The hallmark of autonomous systems is that they are considered to be independent of human intervention in a general sense, and contain the potential to enhance the performance, reduce the costs and improve the safety in a diverse range of application domains. However, a major problem remains that these systems leverage the developments in AI which remain black box. Due to this, the nature of their decision making remains an open concern and their violation of the set policies or regulations in a certain application domain remains a risk. In order to address these concerns, the nature of interaction between humans and autonomous systems should be reimagined. Rather than considering one being the supervisor of the other, the goal should be to investigate the continuous involvement of each other as a teaming problem. Adaptive Human Operator Interaction with Autonomous Systems (AHOI) aims to address these concerns. Our novel research approach brings together a diverse set of researchers involved in various research domains such as Artificial Intelligence (AI), Explainable AI (XAI), human behavioral science, maritime personnel training and human machine interfaces (HMI) to solve the teaming problem between humans and autonomous systems in a use case of collision avoidance in the short sea shipping. We aim to solve this problem at multiple levels.

Researcher(s)

• Promoter: Oramas Mogrovejo José Antonio

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Edge computing has emerged as a novel computing paradigm for the Internet of Things. Compared with the well-known cloud computing, edge computing migrates data computation or storage to the network ''edge,'' near the end users. This approach offers several advantages; it (1) reduces end-to-end latency, (2) reduces congestion and bandwidth consumption in the core network, (3) improves local load balancing capabilities and scalability, and (4) improves privacy and security. When pushing this model to the far edge, sensors and other computing devices have severely constrained capabilities (i.e., computational power, storage, and energy) compared to traditional edge or cloud servers. This significantly complicates the deployment and execution of machine learning (ML) algorithms at the edge, requiring so-called TinyML solutions, that operate in the milliwatt power range and below. To date, TinyML has focused on enabling basic ML on individual low-power sensors and other far edge devices. However, this does not allow the implementation of complex collaborative far edge applications, where many edge devices need to perform a set of coordinated sub-tasks to achieve a global objective. This project aims to address this gap, allowing resource-constrained sensors and far edge devices to collaboratively learn and make decisions. We will study scalable wireless data collection and aggregation techniques, making use of over-the-air computing. Moreover, we will design collaborative resource-aware scheduling and distributed intelligence methods that support adapting TinyML algorithm execution based on limited and variable available resources and energy.

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Currently, there is much research in the field of autonomous navigation. More recently, reinforcement learning (RL) is showing promising results in that field. A type of RL that shows great potential, called model-based RL has some considerable advantages over its model-free counterpart. Notably, it shows potential for safety improvement. Safety is one of the most important challenges that RL in autonomous navigation currently faces.

Researcher(s)

• Promoter: Mercelis Siegfried
• Co-promoter: Anwar Ali
• Fellow: Herremans Siemen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Extended Reality (XR) has become the killer application for future wireless networks. XR is expected to be a major source of traffic for 6G. Use cases include education, health care, and gaming. XR requires accurate and real-time pose information (gesture recognition) to enable seamless experience. Recently, researchers have investigated the use of sub-6 GHz Wi-Fi signals for pose estimation and the results are very promising. Radio waves at these frequencies offer limited resolution due to low bandwidth. On the other hand, mmWave frequencies (>30 GHz) not only offer high data-rates but also high spatial resolution. The improved spatial resolution can benefit pose estimation in XR applications where accurate motion tracking is important for immersive and realistic experience. In this project, my aim is to leverage mmWave signals for pose estimation. This idea of using communications signals for sensing is known as Integrated Sensing and Communication (ISAC). To do this, I aim to collect an extensive and realistic dataset of gestures across several people and environments. I will then develop novel signal processing and deep learning-based algorithms for environment independent and multi-user sensing. Moreover, I will investigate a low power solution for pose estimation on the edge devices. Finally, I will develop a real-time prototype, which projects real-world movements onto the virtual world avatar of the user.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Berkvens Rafael
• Fellow: Bhat Nabeel Nisar

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Due to its strong economic impact, the field of process control has received much research interest over the years. Whilst traditional control methods have been used in the industry for decades, the application of Machine Learning (ML) has not been properly assessed. An interesting novel field withing ML is Reinforcement Learning (RL), which has repeatedly improved the state-of-the-art (SOTA) in the control of complex systems. Consequently, applying this technique to industrial process control has the potential of strongly improving process efficiency. On the one hand, this leads to reduced cost, resource usage and energy requirements for some of the biggest industries worldwide. On the other hand, this opens a new avenue for collaboration between academics and industry. This project aims to research techniques that are centered around applying RL to industrial process control by developing goal-oriented agents that effectively capture the expectations of the user. (1) An agent with an accurate latent world model will be developed with SOTA performance and strong reasoning capabilities. (2) This agent is extended with a reverse imagination model to reconstruct physical states from latent states. State constraints are applied to these physical states based on expert knowledge to create an intuitive framework for guiding the agent. (3) The agent is then transferred from simulation to reality using offline data to align the internal world model with the real-world environment

Researcher(s)

• Promoter: Mercelis Siegfried
• Co-promoter: Anwar Ali
• Co-promoter: Mets Kevin
• Fellow: Troch Arne

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Swarm robotics enables large groups of robots to collaborate on complex tasks. An important barrier that prevents real-world applicability is that robots are generally battery-powered, resulting in an autonomy of a few hours at best. Existing solutions to this problem, such as autonomous charging stations, powered surfaces, and wireless power transfer, rely on the presence of a power grid. This makes them unsuitable and impractical in many situations, where robots are deployed in an ad-hoc manner, or in hard-to-reach locations. In AmbientSwarms, we propose an alternative solutions, where robots are powered using ambient energy harvesting to achieve multi-year autonomy. The major downside of ambient energy, is its variability and unpredictability. As a result, ambiently-powered robots will inevitable suffer from power failures, and intermittently turn on and off. This significantly complicates computing and communication among collaborative robots. In the AmbientSwarms project, we will develop novel methods for intermittently powered swarm robots to communicate with each other and collaboratively perform tasks. Moreover, we will study how mobile recharging stations can be used to improve the task completion success rate of the swarm. To evaluate our solution, both a simulation tool and testbed with hardware prototypes will be developed.

Researcher(s)

• Promoter: Famaey Jeroen
• Fellow: Lemoine Wouter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The current approach to monitoring road quality is based on manual inspections and is labor intensive and relatively expensive. Hybrid AI for Predictive Road Maintenance (HAIRoad) aims to use (hybrid) AI to map the condition of the road network and make recommendations for road maintenance. An efficient and robust data pipeline will be developed using MLOps tools, which allow easy switching between model development and implementation/production. Three demonstrators will illustrate the feasibility of the approach: one with the Port of Antwerp Bruges and two at the municipal level. The demonstrators will allow to validate both the more technical aspects and the market potential. HAIRoad will deliver several innovations such as automated detection of the road conditions, new indicators for road management, sensor fusion by combining information from multiple sensors, and the application of hybrid-AI where we will incorporate physical models of road degradation into data-driven machine learning models.

Researcher(s)

• Promoter: Mercelis Siegfried
• Co-promoter: Anwar Ali
• Co-promoter: Daems Walter
• Co-promoter: Hasheminejad Navid
• Co-promoter: Hernando David
• Co-promoter: Steckel Jan
• Co-promoter: Vanlanduit Steve
• Co-promoter: Vuye Cedric

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The project aims to research data-efficient and scalable grey-box modelling for end-point prediction of unit operations in pharmaceutical production processes. We will research a method that makes use of physics-informed neural networks and few-shot learning to achieve this. To enable broader applicability, we will investigate how to efficiently design and calibrate the models in a real-world setting. This method will yield a thorough understanding of the process state during each individual unit operation and provide a twofold benefit for Janssen Pharmaceutica: (1) increase efficiency and decrease cycle times of commercialized processes, and (2) deliver the Best Process At Launch (BPAL) for New Product Introductions (NPIs).

Researcher(s)

• Promoter: Mercelis Siegfried
• Fellow: Robeyn Michiel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main goal of INNO2MARE is to strengthen the capacity for excellence of Western Slovenian and Adriatic Croatian innovation ecosystems through a set of jointly designed and implemented actions that will support the digital and green transitions of the maritime and connected industries. Based on an in-depth mapping of the ecosystems and needs & gaps analysis, the consortium will formulate a long-term R&I strategy aligned with regional, national and EU strategies, as a visionary framework, and a joint action & investment plan, with concrete steps for building coordinated, resilient, attractive and sustainable maritime innovation ecosystems. To support the joint strategy and provide a model for the future collaborative R&I of the ecosystems' actors, the project will implement three R&I pilot projects that address some of the key challenges related to maritime education and training, security & safety in marine traffic as well as energy conversion and managementsystems' efficiency. These pilots will be the basisfor further development,scale-up and translation of the generated research results into innovative business opportunities through the coordinated mobilisation of public and private funding. The consortium will also implement innovative programmes that will support the engagement of citizens in the innovation processes, knowledge transfer for mutual learning, entrepreneurship & smart skills training and attraction of best talents, involving more than 1.000 participants across the Quadruple Helix. In all the project activities, the two ecosystems will strongly benefit from the sharing of best practices of the Flemish ecosystem, one of the most developed maritime innovation ecosystems globally. The project will contribute to reducing the innovation divide in Europe by systematically connecting the innovation actors within and between the ecosystems and creating synergies in R&I investments' planning and execution, thus developing a true innovation culture

Researcher(s)

• Promoter: Mercelis Siegfried
• Co-promoter: Anwar Ali
• Co-promoter: Daems Walter
• Co-promoter: Demeyer Serge
• Co-promoter: Steckel Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Scheduling is a fundamental component in any computer system. It is the process of arranging and optimizing the execution of (computing) tasks, called jobs. Although there exists an abundance of scheduling algorithms, many systems still rely on the First-Come-First-Served (FCFS) scheduling algorithm as it is considered to be a fair scheduling algorithm that does not require any information on the job sizes or job arrival times. Moreover, given some technical conditions, it can be shown that the response time distribution under FCFS has the best possible decay, which in layman's terms means it is good at avoiding large response times, contrary to other scheduling algorithms that may have a better mean response time (such as Shortest-Remaining-Time-First). For a long time researchers believed that any improvements made to the mean response time of FCFS come at the expense of worsening its tail probabilities (i.e., "There is no such thing as a free lunch"). A very recent, original result showed that this is however not the case. More specifically, it was shown that an algorithm, named NUDGE, can be devised that improves all of the response time distribution quantiles of FCFS. This discovery opened up many new research directions in scheduling, e.g. "How much job size information (if any) is needed to improve upon FCFS?". These new fundamental open problems in scheduling are the topic of this research proposal.

Researcher(s)

• Promoter: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Our society is undoubtedly rapidly evolving towards a fully digital society. These changes and new technologies such as 5G, (I)IoT, Cloud computing, Edge computing, Big Data... and many other new concepts, are getting embedded in our society and daily life. As a consequence, our communication networks and the internet, become very complex and rely on a heterogeneity of technologies never seen or experienced before. Research on new concepts and new aspects of this Next Generation Internet as well as developing tools, techniques and applications cannot be carried out without experimentation. Testing of these newly researched and developed technologies cannot be carried out on systems active in the real world but require experimentation facilities which can mimic the real network in all its aspects. Flemish universities and research organizations have invested in and established a collection of world-class experimentation facilities for these purposes, covering a wide range of technologies, and this proposal aims at establishing a Flemish and Belgian node in a European Research Infrastructure which would integrate all of these testbeds into one single research infrastructure. Scientific communication

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)
• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

Low-power wireless technology tends to be used today for simple monitoring applications, in which raw sensor data is reported periodically to a server for analysis. The ambition of the OpenSwarm project is to trigger the next revolution in these data-driven systems by developing true collaborative and distributed smart nodes, through groundbreaking R&I in three technological pillars: efficient networking and management of smart nodes, collaborative energy-aware Artificial Intelligence (AI), and energy-aware swarm programming. Results are implemented in an open software package called "OpenSwarm", which is verified in our labs on two 1,000 node testbeds. OpenSwarm is then validated in five real-world proof-of-concept use cases, covering four application domains: Renewable Energy Community (Cities & Community), Supporting Human Workers in Harvesting (Environmental), Ocean Noise Pollution Monitoring (Environmental), Health and Safety in Industrial Production Sites (Industrial/Health), Moving Networks in Trains (Mobility). A comprehensive dissemination, exploitation, and communication plan (including a diverse range of activities related to standardization, educational and outreach, open science, and startup formations) amplifies the expected impacts of OpenSwarm, achieving a step change enabling novel, future energy-aware swarms of collaborative smart nodes with wide range benefits for the environment, industries, and society.

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

To deliver on our European 6G vision for the 2030s, and to tackle opportunities and challenges of increasing magnitude, e.g.,sustainability, trustworthiness, green deal efficiency, digital inclusion, there is need for a flagship project, towards the elaboration of aholistic 6G network platform and system. To fill this need, Hexa-X-II is proposed with the ambition of being this flagship project, andof inspiring the world for digital transformation through novel 6G services. Hexa-X-II will work, beyond enabler-oriented research, tooptimized systemization, early validation, and proof-of-concept; work will progress from the 6G key enablers that connect the human,physical, and digital worlds, as explored in Hexa-X, to advanced technology readiness levels, including key aspects of modules /protocols / interfaces / data.Hexa-X II includes: (a) the provision of advanced / refined use cases, services, and requirements, ensuring value for society; (b) thedelivery of the 6G platform blueprint, which will encompass enhanced connectivity for 6G services, mechanisms realizing the"networks beyond communications" vision (sensing, computing, trustworthy AI), efficient network management schemes; (c) therealization of extended validation at system and component level; (d) actions for global impact, while assuring strategic autonomy incritical areas for the EU.Europe is starting from the pole position with 6G research and is leading wireless network technologies today. Now is the time toleverage our joint research ambition with a flagship project that will lead the R&D effort towards end-to-end systemization andvalidation. The Hexa-X-II flagship is a unique effort and a holistic vision, of a 6G system of integrated technology enablers, whichaccomplish "beyond the sum of the parts", and of a "network beyond communications" platform for disruptive economic /environmental / societal impact; these are vital for establishing the European 6G technology leadership!

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Marquez-Barja Johann
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Many Flemish companies are aware of the potential impact of AI, but most SMEs have not yet investigated how AI might affect their business. Given the technology potential, there is an urgency to accelerate the adoption of AI in Flanders. The Flanders AI EDIH accelerates the adoption of AI among (especially) SMEs and public sector organisations by an integrated service offering: (1) Test before invest: initial advice, individual coaching, AI technical feasibility study, legal workshop, Start AI, (2) Skills and training: AI inspiration session, thematic webinar & event, masterclass, AI Summer school, (3) Support to find investments: info session, financial literacy course, matchmaking & finfinder guidance (4) Innovation ecosystem and networking: talent & skills matchmaking, matchmaking on AI supply & demand, matchmaking on joint research, Flanders AI Forum. The Flanders AI EDIH consists of complementary partners who guarantee a cross-sectoral and accessible Flemish operation, with a local physical presence in every Flemish province. The Flanders AI EDIH strengthens existing Flemish initiatives to prevent a fragmentation of available AI innovation services, and actively aligns its service offer through close collaboration with the Flemish Industry Partnership, the Flanders AI Research Programme, the Flanders AI Academy (VAIA), the Flemish Supercomputer Centre (VSC) and Enterprise Europe Network (EEN) Flanders. The Flanders AI EDIH currently offers a number of smart spaces as testing and experimentation facilities, maintain direct links with existing innovation actors and initiatives (such as the Flemish sectorial focused DIHs) and is closely aligned with the Flemish AI policy plan. At European level, the Flanders AI EDIH has ongoing structural collaborations through the AI DIH Network, the Smart Connectivity DIH Network (SCoDIHNet), the EPoSS Smart Systems EDIH Task Force, the Vanguard AI Pilot.

Researcher(s)

• Promoter: Oramas Mogrovejo José Antonio
• Co-promoter: Braem Bart
• Co-promoter: De Schepper Tom

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Autonomous navigation has been gaining much traction recently. As a result, we see autonomy developing in vehicles and finding its way in many transportation sectors (including smart shipping). Nevertheless, the current state-of-the-art (SOTA) technology is not mature enough to have a widespread application at a higher autonomy level (e.g. level 4 and above). The main reason is that these systems are trained on a lot of real-world data, which often lacks accident-prone scenarios. In order to solve this problem, I propose a solution based on data-driven neural simulations that provide realistic data based on real-world samples and generate unsafe scenarios (collisions, accidents, etc.). Moreover, my system also provides safety checks to validate unsafe scenarios and provide safe boundaries for the current autonomous systems.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Anwar Ali
• Co-promoter: Mercelis Siegfried
• Co-promoter: Oramas Mogrovejo José Antonio
• Fellow: Duym Jens

Research team(s)

• Modelling for Sustainability (M4S)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

E-TREND is a research and development initiative focusing on creating decision-making tools that integrate expertise in meteorological forecasting and climate projections for renewable energy sources (RES) in Belgium. It aims to enhance the modeling of wind and photovoltaic energy production and electricity consumption through meteorological ensemble forecasting, climate services, and advanced modeling techniques. The project involves collaboration among Belgian federal scientific institutes and universities to develop and integrate RES generation models into a comprehensive forecasting chain. This effort addresses the integration of current best practices and explores advanced topics beyond conventional methods. The outcomes are designed to support energy sector stakeholders in their operational and planning decision-making processes, with a particular emphasis on incorporating input from Belgian stakeholders to guide research and development efforts. E-TREND's primary research priority aligns with developing forecasting tools for renewable energy production, linked to high-resolution atmospheric weather prediction and regional climate models, aiming to improve the predictability of essential variables for managing renewable energy power production. The project differentiates between "forecasting" for short-term meteorological predictions and "projections" for long-term climate outlooks, offering tools for both applications. Additionally, it contributes to understanding the impact of climate change on energy resources, assisting in the creation of future scenarios for sustainable energy production balance. E-TREND aligns with Belgian and European commitments to increase renewable energy usage, supporting the transition to a net-zero emissions economy by 2050 under the Horizon Europe Framework Program.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)
• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

The wastewater sector is going through a profound transformation with energy efficiency and resource recovery as key priorities in wastewater treatment plants (WWTP) and these installations started to be perceived as Water Resource Recovery Facilities (WRRF). Under this context, the exploitation of data through artificial intelligence tools with the objective of accelerating the transition of WWTP to WRRF has not been fully addressed yet. When compared to treatment technologies, the deployment of AI-powered tools in production is much faster and, therefore, provides immediate benefits. In that sense, three main barriers have been identified in this domain: i) Mechanistic mathematical models involve complex formulations and specific terminology that are difficult to understand for plant operators; ii) WRRF are harsh environments with strong impact on the quality of data; iii) Essential information in WRRFs is limited and not continuously available. In particular, to overcome these challenges, DARROW will build and demonstrate into an operational environment, an innovative, optimised, modular, and flexible data-driven AI solution to make existing WWTP more autonomous, more energy efficient and better prepared for their transformation into WRRF. DARROW will take advantage of existing AI & Data analysis techniques with the final objective of contributing to a greener planet by: i) Reducing energy consumption of WRRF; ii) Reducing Greenhouse Gas Emissions of WRRF; iii) Increasing Resource Recovery iv) Improving water quality.To do so, DARROW gathers the necessary experience, knowledge and resources through a multi-stakeholder approach that covers the whole value chain of the project. It consists of a multidisciplinary team of 8 entities from 4 different EU countries (Spain, Belgium, Germany and Netherlands), among which, 3 RTOs, 1 university,1 NPO, and 3 SMEs to ensure market exploitation (2 industrial companies and 1 water company).

Researcher(s)

• Promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

TrialsNet will deploy full large-scale trials to implement a heterogenous and comprehensive set of innovative 6G applications basedon various technologies such as cobots, metaverse, massive twinning, Internet of Senses, and covering three relevant domains of theurban ecosystems in Europe identified by i) Infrastructure, Transportation, Security & Safety, ii) eHealth & Emergency, and iii) Culture,Tourism & Entertainment. There will be 13 representative use cases developed over wide coverage areas with the involvement ofextended sets of real users in 4 geographical clusters, in Italy, Spain, Greece and Romania. The use cases will be transversal, eachsingle use case will be potentially implementable over different clusters, thus allowing for a holistic evaluation of the network KPIs.Targeting to improve the "liveability" of the urban environment in the different domains, TrialsNet will also pursue the objective to i)understand where current networks are not sufficient to assure the performance needed by the use cases, and to ii) derive the newrequirements for next generation mobile networks. To achieve this, TrialsNet will design and deploy platforms and network solutionswith advanced functionalities based on dynamic slicing management, E2E orchestration, NFV, MEC and AI/ML methods to be trialledon 3GPP and O-RAN network architectures. Design objectives of sustainability and affordability of the deployed systems will be alsotreated with the highest priority. Finally, TrialsNet will also develop appropriate technical assessment frameworks mapping quantitative and qualitative measures andvisualizing the dynamics of the use cases for society acceptance. Proper KVIs will be monitored, proved and refined to provide a sociotechnicalvision towards early adoption of 6G solutions.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

The FOR-FREIGHT project aims to maximise the utilisation of multimodal freight transport capacity and reduce the average cost offreight transport through the development of novel solutions and their integration with legacy logistics systems. This will enablemore effective and sustainable management of goods and freight flows in airports, ports, inland terminals and various logistics nodes,taking into account the requirements of all involved stakeholders, and accounting for economic, environmental and social aspects.Through the FOR-FREIGHT solutions the following functionalities will be delivered: i) real-time door-to-door tracking and statusmonitoring & control of cargo, ii) Decision Support Systems for the optimization of resource utilization based on Digital Twin concept,iii) Increased resilience against large scale disruptive events and increased security of information based on Blockchain technology, iv)Increased sustainability through the implementation of a carbon footprint assessment framework and use of alternative modes oftransportation (subway).To achieve these ambitious goals, the FOR-FREIGHT project engages world-leading T&L stakeholders specializing in different modesof transportation, such as port and airport authorities, terminal operators, airfreights handlers, train operators and major transport/shipping operators and brings them together with leading SW and technology developers, research organizations and innovativeSMEs. This collaboration will drive the deployment of three state-of-the-art multimodal trial facilities, to enable real life trials inoperational environments covering heterogeneous multimodal scenarios, namely, seaport to logistics hub and last mile delivery,seaport to airport (airfreight) and river-port to rail cargo. A cloud-based experimentation platform will be offered by FOR-FREIGHT,integrating access to the three trial sites, and offering advanced monitoring and experimentation tools.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

The introduction of 5G cellular networks opens a huge potential for new industrial time-critical applications and to lower design times of time-critical applications. However, this far, there is no proven evidence that private 5G can offer the reliable and low-latency (<10 ms) end-to-end communication needed to support robust, time-critical control algorithms for teleoperation and collaborative operation in industrial and offshore environments. The ValArch5G project investigates whether private 5G meets the requirements of availability and reliability. Hereto, we develop an end-to-end communication digital twin (based on real measurements), a predictive QoS map and an adaptive closed-loop control strategy. These developments are validated by implementing them in use cases including 1) the teleoperation of an AGV/ASV using a common private 5G network infrastructure, 2) an AGV/ASV controlled over a network, 3) collaborative control between AGV and machine

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Virtual and augmented reality (VR/AR) has arisen as the killer application for future wireless networks. Such applications are expected to require up to several gigabits per second (Gbps) of throughput, as well as millisecond end-to-end latency to enable interactivity. Current wireless local area networking (WLAN) technologies, such as Wi-Fi, cannot attain such high throughputs. However, increasing the radio transmission frequency up to the millimeter-wave (mmWave) band (i.e., 30-300 gigahertz), would support network throughputs up to tens or even hundreds of Gbps. One important hurdle needs to be overcome though: mmWave transmission suffers from high propagation loss and heavy attenuation by obstacles (e.g., walls, people). As such, latency and throughput are highly variable due to user movement and obstacles. In WaveVR we aim to address this issue, and make mmWave technology suitable for future interactive VR/AR applications with mobile devices. We will achieve stable throughput and millisecond latency by introducing the novel concept of context-awareness to mmWave networks. User, network and environmental context (e.g., user location and movement, detected obstacles) will be used to optimize protocols from the data link to the application layer, and enable seamless multi access point transmissions to avoid obstacles. A novel evaluation approach, combining technical metrics with user perception is envisioned.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Lemic Filip

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The radio frequency part of the spectrum is filled with various energy sources that are transmitted, reflected, refracted, diffracted, absorbed and scattered by objects and persons in the real world. The energy is transmitted for other reasons than interpreting the environment: it is transmitting information, data that people or machines are sharing. However, like a lighthouse that signals the location of the shore and simultaneously intermittently lights up that shore, radio frequency transmissions carry both data and information about the environment. In the most generic sense, our objective is to be able to look at the radio frequency spectrum like our eyes look at the visual light spectrum and 'see' what is happening. The major research hypothesis of this proposal is that devices and persons can be counted, identified and tracked between rooms by studying changes in received yet unknown radio signals. If we can prove this hypothesis, the academic breakthrough will lead to a novel class of research focusing on interpreting the real world using the below visual light ambient electromagnetic spectrum.

Researcher(s)

• Promoter: Berkvens Rafael
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

General goal Optimizing the AI training, network installation and forecasting aspects of an RF-based counting system for crowds using both external data and data from the same RF-based counting system.

Researcher(s)

• Promoter: Berkvens Rafael
• Promoter: Hellinckx Peter
• Fellow: Janssens Robin

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Swarm robotics enables large groups of robots to collaborate on complex tasks, which requires low latency many-to-many wireless communication among them. Enabling such communication is still an open issue and is complicated by the fact that robots need to rely on unpredictable ambient energy harvesting for long-term autonomy. Traditionally, multi-hop wireless networks rely on packet-based store-and-forward protocols, where packets are fully transmitted to the next hop, temporarily stored, and then forwarded further towards the destination. Such protocols require a lot of coordination among nodes, are energy hungry, and have a high latency. This makes them ill-suited to satisfy the requirements of mobile and ambiently-powered robot swarms. We instead propose a radically different solution, based on symbol-synchronous wireless transmission, where nodes forward each received data symbol immediately. This allows all nodes in the network to transmit the packet in parallel, reducing latency by several orders of magnitude. This project is the first attempt to apply symbol-synchronous transmissions in a highly mobile environment with severe energy constraints. We will design two energy efficient symbol-synchronous transceivers, based on infrared (IR) and radio frequency (RF) waves respectively. Additionally, we will investigate energy efficient symbol-synchronous network protocols for ambiently-powered robot swarms and develop a robot prototype using both the IR and RF transceivers.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The overall goal of the AI PathFinder project is to support Flemish food companies to develop their AI strategy and thus accelerate concrete AI adoption. This will eventually make them a company that is much more strongly armed for the future, thus maintaining competitiveness and growth. The focus is therefore on inspiring, encouraging and facilitating adoption of AI-based solutions for concrete challenges, needs and opportunities of food companies.

Researcher(s)

• Promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Neuromorphic computing is an emerging field of research. In machine learning, spiking neural networks (SNN) are now widely used to exploit the low-power consumption promise of these brain-inspired systems, saving up to an order of magnitude of energy in inference. Recently, advanced training methods for spiking neural networks have been developed to bridge the performance gap with deep learning, enabling use in real life applications at the edge such as continuous heart rate monitoring in smartwatches or on-sensor detection of dangerous sounds. More precisely, the Liquid State Machine (LSM) a recurrent reservoir-based SNN, has come forward as a simple, yet inherently very powerful computational framework for spatio-temporal data processing. The spike-based processing of time-series in a reservoir allows the LSM to retrieve features in a unique way. There are many open research questions, such as what type of learning best suits the neuromorphic reservoir and how multiple reservoirs can be connected in an optimal way so the most important features are passed through. In this proposal we introduce new spike-based learning rules that will allow us to derive relevant features inside the LSM, optimally connect multiple reservoirs by focusing on the important features and consequently boost the performance of LSM at low power consumption.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Deckers Lucas

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the state-of-the-practice IoT platforms complex decisions based on sensor information are made in a centralized data center. Each sensor sends its information over thereafter a decision is send to actuators. In certain applications the latency imposed by this communication can lead to problems. For this, decisions should be made on the edge devices themselves. This is what the research track on resource and context aware AI is about. We want to develop edge inference systems that dynamically reconfigure to adapt to changing environments and resources constraints. This work is focused on compressing neural networks. In this work we want to extend on the current state-of-the-art on neural network compression by incorporating a knowledge-based pruning method. With knowledge based we mean that we first determine the locations of specific task related knowledge in the network and use this to guide the pruning. This way we can make the networks adjustable to environmental characteristics and hardware constraints. For some tasks in a specific environment, it might be favorable to reduce the accuracy of certain classes in favor of resource gain. For example, the classification of certain types of traffic sign types can be less accurate on highways than in a city center. Based on these requirements we want to selectively prune by locating specific task related concepts. By removing them we expect to achieve higher compression ratios compared to the state-of-the-art.

Researcher(s)

• Promoter: Hellinckx Peter
• Promoter: Mercelis Siegfried
• Co-promoter: Steckel Jan
• Fellow: Balemans Dieter

Research team(s)

• Modelling for Sustainability (M4S)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In recent years, autonomous robotic systems have gained lots of attention from the academic world and industry. The many applications in industrial fields going from manufacturing, mining and surveillance makes the study on autonomous systems interesting with lots of valorization potential. The cost of these autonomous systems is currently extremely high as expensive computational platforms and sensors suites are used to provide necessary levels of safety and autonomy. Using the measurements from different sensors, an environment representation is created to make navigational decisions. While the environment representation determines the complexity of the behavior that can be achieved, the detail stored in this representation is dependent on the available computational resources and sensor data. The goal of this research project is to enable an autonomous agent to select the optimal heterogenous set of sensors to create an environment representation of the appropriate complexity for the current situation. Resource awareness plays an important role in our research as we aim to reduce computational workloads on the autonomous vehicles, which means less expensive computational platforms can be used. Additionally, increased reliably and accuracy in environment perception will benefit the autonomy of these systems. Less expensive autonomous systems while being efficient in the use of resources will benefit and increase the adoption of autonomous vehicles.

Researcher(s)

• Promoter: Mercelis Siegfried
• Promoter: Steckel Jan
• Co-promoter: Hellinckx Peter
• Co-promoter: Steckel Jan
• Fellow: Balemans Niels

Research team(s)

• Co-Design of Cyber-Physical Systems (Cosys-Lab)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Reliability and accuracy are the two fundamental requirements for intelligent transport systems (ITS). The reliability of active perception for situational awareness algorithms has significantly improved in the past few years due to AI developments. Situational awareness can be improved through exchange of information between multiple agents. Making it complex to accomplish high accuracy at low computational cost cooperatively is critical to ensuring safe and reliable transport systems. This research will tackle the main challenges for shared situational awareness that requires perception from multiple sensor streams and multiple agents. This research will tackle the local sensor fusion problem with graph-based deep learning. Local sensor fusion is the fusion at the agent level where multiple mounted sensors will be used to solve a defined task. By exploiting the structural information in multiple modalities, the proposed solution will construct graph-based deep learning. Then distributed fusion will be accomplished by fusing predictions from multiple agents. As a result, the predictions can be fused across multiple agents to produce a richer situational awareness. The advantage of doing distributed fusion is evident in situations where a single agent's perception is not enough. This will be achieved by modeling spatio-temporal graph networks and studying dynamic updates in the graphs. The results will be validated using real-life benchmark datasets and simulation engine.

Researcher(s)

• Promoter: Hellinckx Peter
• Promoter: Mercelis Siegfried
• Co-promoter: Anwar Ali
• Fellow: Ahmed Ahmed

Research team(s)

• Modelling for Sustainability (M4S)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This BOF Docpro project aims to design and characterise a wireless self-sufficient implantable sensor system. This system can be used both for continuous health monitoring as for the specific detection of biomarkers. More specifically, this research project will focus on lactate detection since it is a well-known biomarker for cancer, fatigue, infections and during anaesthesia. In order for the proposed system to thrive, three main aspects should be taken into consideration: in-body energy harvesting, communication, and biosensing. Each of these aspects has its own challenges. Communication needs to be ultra-low power and wireless and able to transmit the sensor data from within the body to a data sink outside the body. Self-sufficiency is also a very important aspect to keep the in-body devices up and running for an extensive amount of time without the need for an external battery source. Using in-body energy harvesting life-long monitoring becomes feasible. The energy harvesting and communication should also match with the biosensors that need to be designed specifically for the biomarker that needs to be monitored. This challenge is accompanied by the fact that the sensors need to be particularly small and low-power and match the energy capabilities of the self-sufficient system. Finally, the complete system should be designed in such a way that the human body does not reject its presence. The combination of these three main aspects introduces countless possibilities in many medical branches where current detection techniques are too shallow and often associated with excessive radiation exposure. Moreover, abnormalities can be detected at an early stage which implies a higher possibility of effective treatment. In the state of the art some individual aspects are already investigated and show great potential. The main challenge and innovation of this DOCPRO proposal is the system design integrating both the in-body energy harvesting (using a hybrid triboelectric nanogenerator and biofuel cell), the in-body communication (ideally using Bluetooth low energy) and the integration of the biosensor (lactate will be used for this investigation). The prototype needs to be characterised, so we have a clear view of the potential for further research. The proposed project will form the initial foundation for a new research-track within IDLab. It will be the first step in a trajectory of interdisciplinary research concerning IDLab (Internet Data Lab) and AXES (Antwerp X-ray analysis, Electrochemistry and Speciation).

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Parrilla Pons Marc
• Fellow: Johns Maby

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The trend towards autonomous drones is currently driving the integration of an increasing number of sensors for safe navigation under all circumstances, forcing algorithms and hardware to be energy efficient and fast. When drone technology continues to mature, deploying swarms of them will enable even more advanced use cases, for example in precision agriculture. Swarms also offer the possibility of sharing both sensory and compute resources, making the swarm act and respond as a single collaborative entity with overall better performance. In this PhD project, we work with real-world multi-sensory data collected by multiple drones and develop a spike-based neuromorphic fusion solution running on custom imec hardware. More specifically, we will focus on the following research questions: - Can we build a low power sensor fusion solution based on spiking neural networks for autonomous drone navigation and obstacle avoidance, running on imec hardware. We will investigate different solutions to perform spike encodings and carry out the learning. A trade off will be made vs power consumption and hardware. - How can collaborative drones, each with their own spike-based neuromorphic fusion solution, communicate with each other in a timely and resource efficient way? Which sensor fusion tasks need to be performed by which nodes in a collaborative setting? - Can we develop efficient techniques for distributed training across multiple spike- based drones to reduce each drone's individual memory and power requirement and, at the same time, lower the convergence time of the swarm?

Researcher(s)

• Promoter: Latré Steven
• Fellow: Van Damme Laurens

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the state-of-the-practice IoT platforms complex decisions based on sensor information are made in a centralized data center. Each sensor sends its information over thereafter a decision is send to actuators. In certain applications the latency imposed by this communication can lead to problems. In real time applications it is crucial for the decision to be taken immediately. For this complex decisions should be made on the edge devices themselves. This is what the research track on resource and context aware AI is about. In this we want to develop inference edge systems that dynamically reconfigure to adapt to changing environments and resources constraints. This work if focused on compressing AI processing blocks, specifically neural networks. In this work we want to extend on the current state-of-the-art methods on neural network compression by incorporating a knowledge-based pruning method. By knowledge based we mean we want to prune a neural network in a context aware manner. A certain application context will impose requirements of the outputs of the network. For example, on a highway is the detection of pedestrians less important than cars. Based on these requirements we want to selectively prune a network by locating knowledge concepts related to the outputs. By selectively pruning them we expect to achieve higher compression ratios compared to the state-of-the-art for context specific networks.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Mercelis Siegfried
• Co-promoter: Steckel Jan

Research team(s)

• Internet Data Lab (IDLab)
• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

PIONEERS brings together four ports with different characteristics, but shared commitments towards meeting the Green Deal goals and Blue Growth socio-economic aims, in order to address the challenge for European ports of reducing GHG emissions while remaining competitive. In order to achieve these ambitions, the Ports of Antwerp, Barcelona, Venlo and Constanta will implement green port innovation demonstrations across four main pillars: clean energy production and supply, sustainable port design, modal shift and flows optimization, and digital transformation. Actions include: renewable energy generation and deployment of electric, hydrogen and methanol vehicles; building and heating networks retrofit for energy efficiency and implementation of circular economy approaches in infrastructure works; together with deployment of digital platforms (utilising AI and 5G technologies) to promote modal shift of passengers and freight, ensure optimised vehicle, vessel and container movements and allocations, and facilitate vehicle automation. These demonstrations form integrated packages aligned with other linked activities of the ports and their neighbouring city communities. Forming an Open Innovation Network for exchange, the ports, technology and support partners will progress through project phases of innovation demonstration, scale-up and co-transferability. Rigorous innovation and transfer processes will address technology evaluation and business case development for exploitation, as well as creating the institutional, regulatory and financial frameworks for green ports to flourish from technical innovation pilots to widespread solutions. These processes will inform and be undertaken in parallel with masterplan development and refinement, providing a Master Plan and roadmap for energy transition at the PIONEERS ports, and handbook to guide green port planning and implementation for different typologies of ports across Europe.

Researcher(s)

• Promoter: Hellinckx Peter
• Promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

A main part of the mission of the RMI is to produce permanent services in order to ensure the security and the information of the population and to support the political authorities in their decision m a king. The development of numerical weather prediction mo dels (NWP) has long been a crucial part of this service. Important developments of the last years are the ever increasing amount of meteorological observations used to improve NWP forecasts through d a ta assimilation and statistical postprocessing , the use of probabilistic ensemble model s that enable better decision support , the ever increasing resolution of the models , and the incorporation of urban effects through land surface schemes . The RMI also o p erationally runs a dedicated road weather mo del since winter 2018 2019 for Belgian highways , giving decision support to traffic agencies such as Agentschap Wegen en Verkeer (AWV) in Flanders High resolution NWP models and data assimilation techniques, en s emble models and the RMI road weather model must continu e to take advantage of the newest scientific developments. Artificial intelligenceis impacting numerous scientific fields , and meteorology is no exception . For example, techniques an d software libraries from Deep Learning are being used in the field of data assimilation and neural networks are starting to be applied to statistica l postprocessing of ensemble forecasts Another important evolution is the availability of crowdsourced meteorologica l data such as from volunteer stations , and new types of sensors such as vehicle sensors, which will be tested in the RMI road weather mo del in the context of the SARWS project. Assimilation of such data can only improve model forecasts if adequate quality control is applied. An innovative new approach is the use of distributed intelligence to perform part of the necessary computations at the le vel of the sensors, before centralizing the data. It isobvious that the RMI would benefit greatly from a univer s ity partner with expertise in artificial intelligence and data science. IDLab University of Antwerp brings such expertise to the table. IDLab performs fundamental and applied research on internet technologies and data science. Within UA , the distributed intelligence group focuses on topics such as distributed and agent based intelligence, scientific machine learning, resource aware AI, and deep reinforcement learning

Researcher(s)

• Promoter: Hellinckx Peter
• Fellow: Casteels Wim
• Fellow: Tabari Hossein

Research team(s)

• Modelling for Sustainability (M4S)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Conventional climate change mitigation alone will not be able to stabilise atmospheric CO2 concentrations at a level compatible with the 2°C warming limit of the Paris Agreement. Safe and scalable negative emission technologies (NETs), which actively remove CO2 from the atmosphere and ensure long-term carbon (C) sequestration, will be needed. Fast progress in NET-development is needed, if NETs are to serve as a risk-hedging mechanism for unexpected geopolitical events and for the transgression of tipping points in the Earth system. Still, no NETs are even on the verge of achieving a substantial contribution to the climate crisis in a sustainable, energy-efficient and cost-effective manner. BAM! develops 'super bio-accelerated mineral weathering' (BAM) as a radical, innovative solution to the NET challenge. While enhanced silicate weathering (ESW) was put forward as a potential NET earlier, we argue that current research focus on either 1/ ex natura carbonation or 2/ slow in natura ecosystem-based ESW, hampers the potential of the technology to provide a substantial contribution to negative emissions within the next two decades. BAM! focuses on an unparalleled reactor effort to maximize biotic weathering stimulation at low resource inputs, and implementation of an automated, rapidlearning process that allows to fast-adopt and improve on critical weathering rate breakthroughs. The direct transformational impact of BAM! lies in its ambition to develop a NET that serves as a climate risk hedging tool on the short term (within 10-20 years). BAM! builds on the natural powers that have triggered dramatic changes in the Earth's weathering environment, embedding them into a novel, reactor-based technology. The ambitious end-result is the development of an indispensable environmental remediation solution, that transforms large industrial CO2 emitters into no-net CO2 emitters.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Oramas Mogrovejo José Antonio
• Co-promoter: Verdonck Tim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Internet connected devices are pervasive in today's world; from smart watches and implants, to thermostats and smart phones, to city-wide deployments of sensors. The wealth of information col-lected by these devices can be used to personalize services and applications, reduce bills and waste in the home, and reduce pollution and traffic congestion. However, there are also great risks. Devices being hacked, network traffic being intercepted, wireless networks tracking devices, breaches of sensitive, and personal, data from corporations and municipal databases, data misuse by contracted third-parties, and fines due to non-compliance are few of the many risks that can form a barrier to system deployment and make it difficult to reap the benefits of the IoT-enabled fu-ture. Moreover, the lack of trusted and secure platforms and privacy-aware analytics methods for secure sharing of personal data and proprietary/commercial/industrial data hampers the creation of a data market and data economy by limiting data sharing. MOZAIK aims to eliminate that barrier by reducing the above-mentioned risks end-to-end, from sensor nodes to the cloud where the data is aggregated, processed and may be stored. To achieve this aim of MOZAIM, we will research on and develop: - a software implementation of a secure and privacy-friendly distributed IoT-data collection and analytics system, considering the whole data cycle, from the generation up to the data sharing, filling important technology gaps through challenge-based and/or user-driven re-search and innovation efforts - an on-demand platform to support businesses and sectors to access expertise, knowledge, algorithms and tools on privacy and security enhancing technologies - a hybrid personal and non-personal data marketplace which ensures respect of prevailing legislation and allows data subjects and data owners to remain in control of their data and its subsequent use.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Currently, my research interests are in future wireless networks and connected applications. I am particularly interested in zero-energy computing and communications to enable a more sustainable Internet of Things, as well as challenging connected applications enabled by future wireless network technologies, such as networked extended reality, collaborative robot swarms, and distributed edge computing.

Researcher(s)

• Promoter: Famaey Jeroen
• Fellow: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The IOF consortium IDLab is composed of academic supervisors at the IDLab Research Group, a UAntwerp research group with members from the Faculty of Science and the Faculty of Applied Engineering. IDLab develops innovative digital solutions in the area of two main research lines: (1) Internet technologies, focusing on wireless networking and Internet of Things (IoT), and (2) Data science, focussing on distributed intelligence and Artificial Intelligence (AI). The mission of the IDLab consortium is to be the number one research and innovation partner in Flanders and leading partner worldwide, in the above research areas, especially applied in a city and its metropolitan surroundings (industry, ports & roads). To realize its mission, IDLab looks at integrated solutions from an application and technology perspective. From an application point of view, we explicitly provide solutions for all stakeholders in metropolitan areas aiming to cross-fertilize these applications. From a technological point of view, our research includes hardware prototyping, connectivity and AI, enabling us to provide a complete integrated solution to our industrial partners from sensor to software. Over the past years, IDLab has been connecting the city and its surroundings with sensors and actuators. It is time to (1) reliably and efficiently connect the data in an integrated way to (2) turn them into knowledgeable insights and intelligent actions. This perfectly matches with our two main research lines that we want to extensively valorise the upcoming years. The IDLab consortium has a unique position in the Flemish eco-system to realize this mission as it is strategically placed across different research and innovation stakeholders: (1) IDLab is a research group embedded in the Strategic Research Centre imec, a leading research institute in the domain of nano-electronics, and more recently through groups such as IDLab, in the domain of digital technology. (2) IDLab has a strategic link with IDLab Ghent, a research group at Ghent University. While each group has its own research activities, we define a common strategy and for the Flemish ecosystem, we are perceived as the leading partner in the research we are performing. (3) IDLab is the co-founder of The Beacon, an Antwerp-based eco-system on innovation where start-ups, scale ups, etc. that work on IoT and AI solutions for the city, logistics, mobility and industry 4.0 come together. (4) Within the valorisation at UAntwerp, IDLab contributes to the valorisation within the domain 'Metropolitanism, Smart City and Mobility'. To realize our valorisation targets, IDLab will define four valorisation programs: VP1: Emerging technologies for next-generation IoT; VP2: Human-like artificial Intelligence; VP3: Learning at the edge; VP4: Deterministic communication networks. Each of these valorisation programs is led by one of the (co-)promoters of the IDLab consortium, and every program is composed of two or three innovation lines. This way, the IDLab research will be translated into a clear program offer towards our (industrial) partners, allowing us to build a tailored offer. Each valorisation program will contribute to the different IOF objectives, but in a differentiated manner. Based on our current experience, some valorisation programs are focusing more on local partners, while others are mainly targeting international and EU funded research projects.

Researcher(s)

• Promoter: Hellinckx Peter
• Promoter: Latré Steven
• Promoter: Mannens Erik
• Co-promoter: Famaey Jeroen
• Co-promoter: Hellinckx Peter
• Co-promoter: Latré Steven
• Co-promoter: Marquez-Barja Johann
• Co-promoter: Mercelis Siegfried
• Co-promoter: Mets Kevin
• Co-promoter: Oramas Mogrovejo José Antonio
• Co-promoter: Saldien Jelle
• Co-promoter: Verdonck Tim
• Co-promoter: Weyn Maarten
• Fellow: Braem Bart
• Fellow: Braet Olivier

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In recent years, there has been increased interest in the field of multi-agent reinforcement learning. For tasks where cooperation between agents is required, researchers are looking towards techniques to allow the agents to learn to communicate while simultaneously learning how to act in the environment. Current state-of-the-art techniques often use broadcast communication. However, this is not scalable to real world applications. Therefore, I want to develop methods to make this communication more efficient. The goal of this research project is to reduce the amount of messages that are sent, while still maintaining the same performance. To reach this goal, I will look at techniques to communicate with a variable amount of agents, at techniques to limit communication using relevance metrics and signatures and at techniques to encourage hopping behavior in agents. The methods proposed in this research project are essential to be able to create scalable control applications by distributing them in combination with scalable learned communication. The developed methods will be validated on simulations of traffic light control.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Mercelis Siegfried
• Fellow: Vanneste Astrid

Research team(s)

• Internet Data Lab (IDLab)
• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

In this project we will focus on designing a model for representation learning that will enable the detection of pollution in microscopic samples at the earliest time possible from hyperspectral images (HSI). Current methods for this task operate of on top of RGB images derived from HSIs. Taking this into account we will focus our efforts on designing a method capable of analyzing the full raw data cube that composes each HSI sample and identifying potential signals to enable the accurate detection of pollution in the sample. In addition, as industrial customers become increasingly aware of the growing maintenance costs and downtime caused by the unexpected machinery failures, predictive maintenance solutions for biopharma companies gain more interest to maintain a competitive advantage. To address this issue, we will investigate methods to analyze data traces coming from different sources, e.g. computer logs, operator reports, quality of the collected samples, etc., in order to identify temporal patterns that can serve as strong indicators of a potential anomaly that will occur in the near future on the monitored systems. Finally, for both of the tasks mentioned above, model explanation algorithms will be investigated and designed so that the predictions made by their respective models can be justified. Moreover, these explanation algorithms, will serve to debug the trained models and assess their validity and robustness towards artifacts, e.g. biases, data leakage, etc., introduced during the training stage.

Researcher(s)

• Promoter: Oramas Mogrovejo José Antonio

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project is devoted for the maintenance of the City of Things Hercules infrastructure . Within this project, we have developed the CityLab testbed which is a wireless edge computing platform for smart cities. This provides experimental access to wireless networking infrastructure, edge computing infrastructure and smart city sensors.

Researcher(s)

• Promoter: Hellinckx Peter
• Promoter: Latré Steven
• Promoter: Mannens Erik

Research team(s)

• Modelling for Sustainability (M4S)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The pharmaceutical and chemical industries are perpetually challenged by the need to enhance efficiency, scalability, adaptability, and sustainability in their processes, all while adhering to stringent regulations. It is here that artificial intelligence (AI) emerges as a promising solution, offering innovative and efficient means to navigate these complexities. At IDLab, our expertise in employing AI as a problem-solving tool in these sectors has been marked by significant achievements, particularly through our Catalisti-ICON project, DAP2CHEM. We successfully demonstrated the potential of Reinforcement Learning (RL) and Explainable AI (XAI) to improve operational efficiency by 25-35% and resulted in reduced energy and raw material consumption, further enhancing sustainability. Our technology has made considerable strides in two critical areas of AI application in high-risk industries: maintaining operational safety and ensuring transparency, while significantly increasing the efficiency of the process. We have utilized RL in a safety-conscious manner, ensuring that operations are reliable and remain within established safety parameters. At the same time, our work with XAI has yielded humanunderstandable explanations for AI decisions, significantly enhancing the transparency of our technology. Building upon these achievements, the goal of this project is to increase the adaptability and scalability of our technology across different operations and production scales. We also aim to enhance training efficiency, increase automation, and make XAI explanations more intuitive for users with various levels of expertise. This direction aligns perfectly with our ongoing commitment to satisfy a wider range of industry needs. The improved transferability and scalability of the technology will increase the level of commercial readiness in order to valorize it in the Flemish and international chemical and pharmaceutical sector. A substantial component of our roadmap is the valorization of the technology. The project aims to enhance its commercial readiness by ensuring the technology is adaptable, scalable, and user-friendly, catering to the wide-ranging needs of the chemical and pharmaceutical industries. Recognizing a promising market demand, the formation of a spin-off company is a viable consideration, with the potential to provide specialized AI services in these sectors. Aiming to be a key player in the digital transformation of the chemical and pharmaceutical industries, we envision a future where our advanced AI solutions become integral to their operational efficiency and sustainability.

Researcher(s)

• Promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In recent years, edge computing has emerged as a novel computing paradigm for the Internet of Things (IoT). It reduces end-to-end latency, congestion, bandwidth consumption, and improves local load balancing capabilities and scalability in terms of resource and energy consumption. On the other hand, when pushing this model to the far edge, sensors and other computing devices have severely constrained capabilities (i.e., computational power, storage, and energy) compared to traditional edge or cloud servers. This significantly complicates the deployment and execution of machine learning (ML) algorithms at the edge. This problem is being addressed by the TinyML community, by allowing individual low-power sensors and other far edge devices to run basic ML algorithms. However, this progress is insufficient to implement complex far edge applications, where edge device is in an environment or context that slowly changes over time. At the same time, due to the massive increase in IoT devices, more and more materials and batteries are being used. The combination of these two trends will require new methods to continue processing sensor data in an optimal way without further burdening the earth and the environment. The IOF POC Saints project aims to fill this gap by enabling sensors and peripherals with limited resources (materials, energy, and environmental impact) to learn and make decisions by aligning their activities with the availability of computing and energy sources on sensor equipment with limited resources. This by bringing together various innovations that have been developed within IDLab, applying them to these application domains and taking the first steps to valorize this in various domains.

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI SENSAI

Researcher(s)

• Promoter: Van Leekwijck Werner

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Flanders AI Research Program focuses on demand-driven, leading-edge, generic AI research for numerous applications in the health and care sector and industry, for governments and their citizens. The requirements were indicated by users from these application domains.

Researcher(s)

• Promoter: Mannens Erik
• Co-promoter: Calders Toon
• Co-promoter: Daelemans Walter
• Co-promoter: Goethals Bart
• Co-promoter: Latré Steven
• Co-promoter: Laukens Kris
• Co-promoter: Martens David
• Co-promoter: Sijbers Jan
• Co-promoter: Steckel Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The objective is the creation of advanced hybrid modelling techniques for computational fluid-dynamics and evaluating their accuracy and computational workload compared to advanced first-principles models.

Researcher(s)

• Promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project is part of the IMEC Frame Agreement and is being given as structural investment for fundamental research based on yearly set KPIs from the group to IMEC. This A-budget is defined within the IMEC Way of Working and part of the frame agreement of the University of Antwerp and IMEC.

Researcher(s)

• Promoter: Mannens Erik

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

There is a lack of reliable information regarding the European vegetation's current elemental composition and the factors that affect it. We investigate 183928 georeferenced records on woody plant's foliar concentrations of Calcium (Ca) Magnesium (Mg), and Sulfur (S) from published databases, which allow us to create European maps of foliar Ca, Mg, and S foliar concentrations using machine learning at a resolution of 1 km^2 for woody plants. We aim to understand the relationship of ecological processes particularly nutrient cycling in the complex ecosystem analysis. For this, we develop a statistical predictive model of foliar concentration of Ca, Mg, and S.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Narsingh Vaidehi

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the emerging field of low power AI for deployment at edge devices, Spiking Neural Networks (SNN) are gaining traction as prime candidate technology due to initial results of spiking neuromorphic systems saving up to one or two order of magnitude in energy for inference tasks. While today's SNNs are typically trained in the cloud using variants of the traditional backpropagation method, future applications will benefit from on-device adaptation and learning capabilities. Spike-Timing-Dependent Plasticity (STDP), an interesting brain-inspired local learning alternative that uses the temporal factor of spike events for learning, has shown promising results for unsupervised feature learning, and can be deployed for on-device learning. However, for training on specific tasks, STDP needs to be extended with a third factor in the form of a success signal to steer the learning process. The existing three-factor learning rules can be characterized by having different and somewhat ad-hoc definitions for the third factor which may or may not work well in particular applications. This proposal will investigate new SNN training methods that combine STDP learning with formal methods from Temporal Logic to define structured reward signals that are applicable to a wide range of supervised, self-supervised and reinforcement learning applications, and allow for distributed deployment. Enhanced SNNs will open up a wealth of opportunities for smart industries, health, environment etc.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Van Damme Laurens

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Due to its strong economic impact, the field of process control has received much research interest over the years. Whilst traditional control methods have been used in the industry for decades, the application of Machine Learning (ML) has not been properly assessed. An interesting novel field withing ML is Reinforcement Learning (RL), which has repeatedly improved the state-of-the-art (SOTA) in the control of complex systems. Consequently, applying this technique to industrial process control has the potential of strongly improving process efficiency. On the one hand, this leads to reduced cost, resource usage and energy requirements for some of the biggest industries worldwide. On the other hand, this opens a new avenue for collaboration between academics and industry. This project aims to research techniques that are centered around applying RL to industrial process control by developing goal-oriented agents that effectively capture the expectations of the user. (1) An agent with an accurate latent world model will be developed with SOTA performance and strong reasoning capabilities. (2) This agent is extended with a reverse imagination model to reconstruct physical states from latent states. State constraints are applied to these physical states based on expert knowledge to create an intuitive framework for guiding the agent. (3) The agent is then transferred from simulation to reality using offline data to align the internal world model with the real-world environment.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Mercelis Siegfried
• Fellow: Troch Arne

Research team(s)

• Modelling for Sustainability (M4S)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Current Internet of Things (IoT) devices are not designed to be sustainable. For example, large batteries are often equipped to guarantee that sensing and transmitting tasks can be performed at fixed intervals for years on end. This significantly affects the form factor, cost, and ecological footprint of IoT devices. In this project, we will design and develop sustainable systems that are able to harvest various renewable energy sources , including but not limited to solar, thermal and kinetic energy. The system will consist of (1) a power management module that includes energy harvesting hardware and energy storage units (i.e. batteries or supercapacitors), (2) sensors for environmental monitoring, (3) a low-power microcontroller unit (MCU) and (4) a low power communication module. Additionally, we will implement intelligent algorithms on this system to make it energy-aware. This will allow the system to adapt its behaviour based on its current and future available energy, effectively improving its reliability and energy efficiency. The sustainable system has a broad application potential, ranging from solar-powered air quality measurement units in Belgium to low-power thermal energy harvesting devices for climate research in Iceland. In this project, we will evaluate our sustainable system with the latter use case.

Researcher(s)

• Promoter: Weyn Maarten
• Fellow: Pappinisseri Puluckul Priyesh

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Authorities have grown concerned over the negative impact of in-car routing services on smooth, safe and green mobility, as these often fail to consider the social cost inherent to the usage of the road by their users. Private partners (such as intermediaries, end-user service providers or mobility consultants) are increasingly involved in rectifying the worrisome aspects of these routing services, though still face the challenge on how to go from public mobility goals to impactful policy advice or route guidance. The key aspects of this challenge are the lack of: (i) a large-scale and robust methodology to quantify the social cost of traffic on a road network; and (ii) academic knowledge on how to implement impactful routing advice, e.g., via altruistic rewards. In OptiRoutS, three industrial partners (Be-Mobile, Movias and TML) and one public partner (AWV) team up with four academic partners (IDLab-Antwerp, IDLab-Ghent, CIB-KUL and SMIT-VUB) to address these challenges and build services that contribute to smoother, safer and more sustainable mobility.  The innovations in OptiRoutS will strengthen the partners positions in two promising markets – traffic policy support and interactive traffic management, thus providing significant scope for valorization. 

Researcher(s)

• Promoter: Mercelis Siegfried
• Co-promoter: Mannens Erik

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Changes in climate can greatly affect the phenology of plants, which can have important feedback effects, such as altering the carbon cycle. These phenological feedback effects are often induced by a shift in the start or end dates of the growing season of plants. The normalized difference vegetation index (NDVI) is a simple indicator that can be used to determine whether the area being observed contains green vegetation and can be used to approximate the growing season of plants. In this project, we apply machine learning techniques to investigate the relationship between soil temperature and the NDVI curve in a unique ecosystem in Iceland and to find out whether this relationship is modulated by climatic variables.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Bussmann Bart

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Exploring the transformative power of real-time localization services (RTLS) in the construction industry. Our sessions delve into RTLS applications for equipment and personnel tracking, enabling improved project coordination and safety measures. We create valuable insights on implementation strategies, operational benefits, and industry case studies.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The 6GStart project will facilitate the preparation activities of the European Smart Networks and Services Joint Undertaking (SNS JU)Initiative. This work will maintain the European momentum and leadership in 5G achieved through the 5G PPP and carry it forward tothe new 6G SNS JU. It will bring the relevant players together to prepare the SNS JU by building on the work done to date in the 5GPPP. This approach will contribute significantly to Europe having a leading role in the definition, provision, and exploitation of 6G by2030.The 6GStart project will ensure the inter-project collaboration structures and mechanisms will be established and in place by the timethe first phase projects of the SNS JU start. As such, the 6GStart project will ensure the fast launch of the new SNS partnership and theavailability of an efficient operational infrastructure for the inter-SNS-project coordination. The infrastructure for the 50+ ongoing 5GPPP projects will also be supported.The 6GStart Project will also orchestrate collaborations, and capture and promote the achievements of the new 6G SNS initiative andthe ongoing 5G PPP by facilitating their activities in inter-project working groups and maintaining links to the NetworldEuropecommunity and the 5G-IA membership.The 6GStart project will support the running of two editions of the EuCNC&6G Summit events in 2023 and 2024, as well as assistingthe organisation of the Global 5G/6G events based on the inter-regional MoUs managed by the 5G IA, contributing to the strategy ofpromoting the European achievements in the wider ICT sector.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

The idea that humans learn by doing forms the basis of modern training methodologies, which prioritize practice over theory. However, practical training requires inexperienced students to operate expensive and possibly dangerous equipment under the constant supervision of an expert trainer. Advances in virtual reality (VR) provide a potentially much safer and cost-effective practical training solution. Current VR systems, however, do not allow lifelike interactions among multiple users, and can thus not support collaborative virtual learning. Several barriers stand in the way of a new generation of interactive multi-user VR experiences: (i) the use of wired VR headsets, (ii) unintuitive virtual avatar control, and (iii) cybersickness due to desynchronization. INTERACT aims to break these barriers by developing a flexible wireless network solution for VR headsets connected to an edge-cloud VR training platform that keeps the user's actions and movements synchronized in both space and time.

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

5GECO (5G Intelligent Radio and Transport Edge Network Cross-Optimisation) aims to reduce the Open 5G RAN Total Cost Ownership (TCO) of Mobile Networks (MN) for ultra-dense urban and private industrial deployments. This will be achieved by developing an Intelligent Neutral Host (INH) platform with end-to-end (E2E) management, supported by orchestration and control capabilities that allow the INH operator to control its equipment, for example the Radio Access Network (RAN) and the Transport Network (TN), can share and its spectrum, whether licensed or shared, with other MN operators so that they can densify their coverage in the most cost-effective way, saving a private network owner's money earn with his private network.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Mannens Erik

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

CHAI will transform chemical process control and stability analysis by fully embracing the vision of hybrid AI. In CHAI, smart AI tools are developed for next-generation chemical process control, by explicitly coding expert knowledge into hybrid AI models. Currently, process engineers need many years of experience and training before they can tackle the most challenging problems completely independently. By embracing hybrid AI, process engineers and operators can be empowered by giving each of them access to (1) everyone's experience and expertise, and (2) a smart system that performs outcome predictions and proposes control actions. The CHAI consortium expects efficiency gains and a better understanding of product characterization; CHAI aspires to influence R&D efficiency and production through cost reduction, and to eliminate the risk of recalling millions of euros of products that do not meet quality standards.

Researcher(s)

• Promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The standard management capabilities of commercially available devices in a combined wired/wireless (Ethernet, WiFi, Bluetooth) environment does not allow to meet the strict requirements for end-to-end (E2E) delays and jitter. VELOCe will make improvements and add extensions to the latest WiFi 6/6E and LE Audio standards and benchmark these. Specifically, VELOCE E2E, will develop compatible mechanisms to reduce delays caused by communication and full control and verification of audio processing, and real-time device and network settings adaptable based on E2E performance measurements.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Building Information Modeling (BIM) provides insight into the design and planning of a building. However, the benefits of this digital display are not yet fully exploited at the construction site. Schedule updates today are often still done manually and are prone to errors, leading to errors, delays and cost overruns. BoB solves these problems by linking BIM to real-time progress and activity data on the construction site. BoB will use AI-driven technology to detect collective activities (e.g. pouring concrete, formwork, digging) and automatically link the current state of the building from image data to the BIM design. This gives site stakeholders much-needed visibility into actual progress, reduces cost overruns, increases efficiency, prevents errors and reduces construction waste. The connected data platform designed in BoB will be a stepping stone to a fully connected digitized construction site.

Researcher(s)

• Promoter: Oramas Mogrovejo José Antonio

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objective of the study is to sustain and justify current and future answers to the following question: assuming GNSS-like ranging signals but broadcast from LEO satellites, and with freedom to select different carrier frequencies and signals, what are the main benefits and challenges to ensure PVT, in a parametric way, from the UE perspective.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Flemish AI research program aims to stimulate strategic basic research focusing on AI at the different Flemish universities and knowledge institutes. This research must be applicable and relevant for the Flemish industry. Concretely, 4 grand challenges 1. Help to make complex decisions: focusses on the complex decision-making despite the potential presence of wrongful or missing information in the datasets. 2. Extract and process information at the edge: focusses on the use of AI systems at the edge instead of in the cloud through the integration of software and hardware and the development of algorithms that require less power and other resources. 3. Interact autonomously with other decision-making entities: focusses on the collaboration between different autonomous AI systems. 4. Communicate and collaborate seamlessly with humans: focusses on the natural interaction between humans and AI systems and the development of AI systems that can understand complex environments and can apply human-like reasoning.

Researcher(s)

• Promoter: Hellinckx Peter
• Promoter: Latré Steven
• Co-promoter: Calders Toon
• Co-promoter: Daelemans Walter
• Co-promoter: Goethals Bart
• Co-promoter: Laukens Kris
• Co-promoter: Martens David
• Co-promoter: Sijbers Jan
• Co-promoter: Steckel Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

5G is the most energy-hungry mobile technology yet, reaching the limit of what the planet and society can environmentally and practically afford. There is already plenty of research towards sustainable and zero-energy end-devices, but huge gains in the energy efficiency and sustainability of the power-hungry network infrastructure behind them are still to be achieved. SAMBAS' project vision represents a holistic approach in driving a significantly more sustainable beyond-5G wireless communications network, where joint considerations of radical innovations at radio, network, and service levels will lead to critically reduced power needs. SAMBAS contributes towards this goal by developing an innovative sustainable millimetre wave (mmWave) micro base station (μBS) that makes effective use of renewable energy harvesting in combination with extremely energy-efficient hardware, and communications protocols to reduce power consumption. At the networking level, we aim to reduce signalling overhead and energy requirements by an order of magnitude through distributed in-band context dissemination and energy-aware networking. Finally, through joint energy-aware network and cloud resource optimization, a sustainable end-to-end mmWave-based system will be developed. We target beyond-5G performance in terms of latency, ultra-high capacity data rates, reliability, and range, while targeting a significant reduction in the reliance on non-renewable energy sources. An integrated prototype will be validated via a multi-user indoor interactive virtual reality application, and an outdoors vehicular communications application.

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Traditional machine learning techniques focus on developing predictive models that have the sole purpose of obtaining a high degree of accuracy on a given data set. These types of models exploit any type of association between the input and target variables that may increase the performance. However, in practice, the training and test distribution often differ significantly, resulting in unreliable and failing models. The key to learning generalizable models that work in a broad range of environments (and that are not affected by small changes in the test distribution) lies in learning causal predictive features. However, learning causal models under changing environments and in systems with hidden confounders is an unsolved problem and is directly connected to the generalisation gap. In this project, we aim to use the novel framework of causal machine learning to develop algorithms that can handle changing environments. More specifically, this project focuses on learning invariant and causal representations from data using causal machine learning. The results are models that are proven to be more generalizable, can cope with interventions, and are able to extract interpretable causal relations directly from data.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Nys Jannes
• Fellow: Mortier Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The European transport faces major challenges in terms of safety, greenhouse gas emissions, traffic congestion and its derived costs. In addition, the development of disruptive technologies and emergence of new mobility solutions generate a revolution in transport network and traffic management. In this context, TANGENT aims to develop new complementary tools for optimising traffic operations in a coordinated and dynamic way from a multimodal perspective and considering automated/non-automated vehicles, passengers and freight transport. TANGENT will research on advanced techniques on modelling and simulation, such as prediction and simulation models for future demand & supply of transport; optimisation techniques for balancing the demand flows between the means of transport; and users travel behaviour modelling. As result, a set of applications for decision-making support will be delivered creating a framework for coordinated traffic and transport management, encompassing an enhanced mobility information service and dashboard with associated APIs and advanced functionalities with a two-fold approach: to provide real-time traffic management recommendations and to support Transport Authorities to design network-wide optimal strategies. The framework also aims at supporting a multi-actor cooperation approach for transport network management by enabling communication channels. In this way, the services target to different actors in traffic management. The results will be tested in three case studies: Rennes (FR), Lisbon (PT), Great Manchester (UK) and a virtual case study in Athens (HE)with real data from various modes of transport, under different traffic events such as bottlenecks, accidents, pedestrian flow etc. The impact will be assessed to reach expected reduction targets of 10% in travel time, 8-10% in CO2 emissions, 5% of accidents, 5-10% increase in use public transport and use of active modes or 10% of economic costs due to a more efficient management.

Researcher(s)

• Promoter: Hellinckx Peter
• Promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Demand forecasting in retail still suffers from the so called bullwhip effect: a small change in point-of-sale demand can cause a large fluctuation in demand at the wholesale, distribution center and supplier. For the food retail chain this materializes into (i) the inability to cope with unexpected events or (ii) to further reduce food waste, and (iii) a weaker position towards e-commerce. AI4FoodLogistics aims to tackle these challenges - focusing on fresh food delivery - by addressing key shortcomings of current tactics. A highly reliable, just-in-time delivery experience for fresh food is targeted, that leverages a novel data architecture capable to propagate data across the value chain in a more scalable, cost-effective way. The consortium spans the full value chain, from farm to fork, and will focus on advancing state-of-the art technology to increase the trustworthiness of demand forecasting, logistics scheduling and personalized recommendations. Key objectives of AI4FoodLogistics are lowering overall logistics cost (ca. 13M euro/year) decreasing food waste in Flanders (at least 15M euro/year), and increasing the share of locally produced healthy food. The outcome will be validated by combining a simulator with in-the-field-validated data-driven models.

Researcher(s)

• Promoter: Mercelis Siegfried
• Co-promoter: Mannens Erik

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Deep learning has surely become a buzzword in the last decade, but rightly so: it is an extremely powerful tool that learns from large amounts of past data, which has significantly outperformed the previous state-of-the-art practices in image processing, language translation, speech and object recognition, biomedicine, drug design, etc. It is ubiquitous in our daily realities - Google Translate, Google Maps, Alexa, Siri, our phone's face or fingerprint unlock feature all rely on deep learning, and it will be of crucial importance for self-driving cars. The practical success of deep learning, however, goes far beyond theoretical understanding. How do deep neural networks work and learn? How well will the network generalize to unseen data? When does it fail, and how can this be avoided? My goal is to shed some light on the last question, by trying to identify the classes of problems for which deep learning performs poorly. In particular, we plan to examine some problems where we would expect topological data analysis to outperform the results obtained with deep neural networks. Topology studies shape, and we expect it to be better in detecting the number of connected components, holes and voids in higher dimensions, or shape convexity; but recent results indicate that the same might be true for detecting shape curvature. We plan to investigate this experimentally, by comparing the results obtained with the two approaches, on a number of diverse synthetic datasets and data available in the literature. In addition, deep learning is expected to underperform when there is not a lot of data available, or when the data is noisy - we will therefore also include such scenarios in our computational experiments. Topological features can thus be recommended as an alternative to deep learning whenever they promise a superior performance, but the findings will also provide us with inspiration on how to improve existing deep architectures, with, for example, an additional network layer for topological signatures, or topological loss functions for network's prediction error.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Update and maintain the AI model every quarter. imec project on recycling electronic waste automatically instead of manually by using an AI model. The AI model was developed in a previous project and this project is for the maintenance of the model and making the necessary updates.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Framework agreement to submit to research projects of the Agentschap Telecom (Netherlands). Possible topics are : - Dynamic Spectrum Management & Sharing - Exploration of the roles of the Netherlands Radiocommunications Agency in the energy transition - etc.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The project targets (i) a novel, virtual and distributed data ecosystem for food delivery to physical shops that becomes hyper responsive and efficient thanks to (ii) more accurate forecasting and personalization models that use enhanced AI and scheduling technologies to (iii) optimize the end-to-end logistics from farmers to Distribution Centers (DCs) to stores and customers.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Hellinckx Peter
• Co-promoter: Verdonck Tim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objective of the 'Smart Port' COOCK project is to improve operational efficiency within the context of the port, by applying intelligent technologies, aimed at SMEs, by increasing digital maturity through data-driven digitization. The proposed COOCK focuses on two target groups: the value chain within a port context, such as terminal operators, skippers, agents, transport companies, forwarders, shipping companies, rail operators, port authorities, ... and also technology integrators: start-ups, scale-ups, IT- companies, etc. that are active in implementation processes in a port environment.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

VITAL 5G - The VITAL-5G project has the vision to advance the offered transport & logistics (T&L) services by engaging significant logistics stakeholders (Sea and River port authorities, road logistics operators, warehouse/hub logistic operators, etc.) as well as innovative SMEs and offering them an open and secure virtualized 5G environment to test, validate and verify their T&L related cutting-edge Network Applications (NetApps). The combination of advanced 5G testbeds (offered through participating MNOs / vendors) with vertical specialized facilities and infrastructure (offered by participating key logistics stakeholders) through an open service validation platform (repurposed and created by the project) will create a unique opportunity for third parties such as SMEs to validate their T&L related solutions and services utilizing real-life resources and facilities, otherwise unavailable to them. The platform will provide to 3rd party experimenters, the necessary testing and validation tools, offering them a trusted and secure service execution environment under realistic conditions that supports multi tenancy. Such an elaborate validation mechanism will allow for the further refinement and fine-tuning of the provided services fostering the creation of new services and the evolution of existing ones, while boosting the SME presence in the emerging 5G-driven logistics ecosystem. The VITAL-5G project plans to showcase the added-value of 5G connectivity for the European T&L sector by adopting a multi-modal approach containing major logistics hubs for freight and passengers (sea ports, river ports, warehouse / logistics hubs, highways, etc.) as well as the respective stakeholders (road operators, port authorities, 3rd party logistics (3PL) operators), thus creating an end-to-end chain of connected T&L services accommodating the entire continent.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

DAEMON - The success of Beyond 5G (B5G) systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone for NI design; indeed, AI models have proven extremely successful at solving hard problems that require inferring complex relationships from entangled and massive (e.g., traffic) data. However, AI is not the best solution for every NI task; and, when it is, the dominating trend of plugging 'vanilla' AI into network controllers and orchestrators is not a sensible choice. Departing from the current hype around AI, DAEMON will set forth a pragmatic approach to NI design. The project will carry out a systematic analysis of which NI tasks are appropriately solved with AI models, providing a solid set of guidelines for the use of machine learning in network functions. For those problems where AI is a suitable tool, DAEMON will design tailored AI models that respond to the specific needs of network functions, taking advantage of the most recent advances in machine learning. Building on these models, DAEMON will design an end-to-end NInative architecture for B5G that fully coordinates NI-assisted functionalities. The advances to NI devised by DAEMON will be applied in practical network settings to: (i) deliver extremely high performance while making an efficient use of the underlying radio and computational resources; (ii) reduce the energy footprint of mobile networks; and (iii) provide extremely high reliability beyond that of 5G systems. To achieve this, DAEMON will design practical algorithms for eight concrete NI-assisted functionalities, carefully selected to achieve the objectives above. The performance of the DAEMON algorithms will be evaluated in real-world conditions via four experimental sites, and at scale with data-driven approaches based on two nationwide traffic measurement datasets, against nine ambitious yet feasible KPI targets.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Camelo Botero Miguel
• Co-promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

DEDICAT 6G - In future 6G wireless networks, it is imperative to support more dynamic resourcing and connectivity to improve adaptability, performance, and trustworthiness in the presence of emerging human-centric services with heterogeneous computation needs. DEDICAT 6G aims to develop a smart connectivity platform using artificial intelligence and blockchain techniques that will enable 6G networks to combine the existing communication infrastructure with novel distribution of intelligence (data, computation and storage) at the edge to allow not only flexible, but also energy efficient realisation of the envisaged real-time experience. DEDICAT 6G takes the next vital step beyond 5G by addressing techniques for achieving and maintaining an efficient dynamic connectivity and intelligent placement of computation in the mobile network. In addition, the proposal targets the design and development of mechanisms for dynamic coverage extension through the exploitation of novel terminals and mobile client nodes, e.g., smart connected cars, robots and drones. DEDICAT also addresses security, privacy and trust assurance especially for mobile edge services and enablers for novel interaction between humans and digital systems. The aim is to achieve (i) more efficient use of resources; (ii) reduction of latency, response time, and energy consumption; (iii) reduction of operational and capital expenditures; and (iv) reinforcement of security, privacy and trust. DEDICAT 6G will focus on four use cases: Smart warehousing, Enhanced experiences, Public Safety and Smart Highway. The use cases will pilot the developed solutions via simulations and demonstrations in laboratory environments, and larger field evaluations exploiting various assets and testing facilities. The results are expected to show significant improvements in terms of intelligent network load balancing and resource allocation, extended connectivity, enhanced security, privacy and trust and human-machine interactions.

Researcher(s)

• Promoter: Marquez-Barja Johann
• Co-promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

In manufacturing solutions, it is important to quickly educate the operators for their specific task at hand. In this project, the goals is to develop an ergonomic model (digital twin) of a workcell (with tools as robots, equipment, tools). This consists of several aspects: - VR enabled assessment -> 'personalized' training (learning and automated examination by assessing behaviour)​ - FOCUS on balance between: ergonomics, well-being and productivity​ - Deriving mental load and ergonomics factors for advise on improved workcell layout​ ​

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

COVID stressed the need for accurate monitoring of crowds (including density estimation). However, the market solutions currently lacs privacy by design, accuracy, and applicability.​ Our solution is to automatically monitor the crowd by​ combining radar and sensing of ubiquitous radio frequency communication.​ Like the visual spectrum, the radio spectrum is constantly lit up by energy sources.​ Reusing these sources is efficient, and yet really hard to identify people afterwards.​ What we need to prove, is that it can be accurate.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the pursuit of developing an AI chip that can efficiently support a quadrillion parameter neural network model it is of utmost importance to define the right AI applications and their AI capabilities that can drive this chip. In this project, the goal is to have an early detection of future applications and required AI capabilities, to identify possible algorithmic approaches, and to allow dedicated technology projects as follow-up. For this, we do a systematic review of future AI evolutions and aim at defining a specific moonshot and roadmap to follow

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project originates from the known interest of the Port of Antwerp in finding an innovative and cost-effective way to detect and prevent overloading of the bollards since the quay walls need to be able to cope with the ever-increasing loads and operational times. Overloaded bollards can become a danger to port operations, resulting in e.g. ship ropes that come loose and vessels hitting the docks or bollards that are pulled from the quay towards the vessels. The port initially searched for existing solutions on the market but rapidly realized that there was not such a "market-ready" solution available to tackle this challenge and therefore launched a call for proposals. ID lab proposed an intelligent early-warning system of interconnected bollards to monitor port infrastructure solution, which went beyond expectations in terms of operational value, by combining two of our expertise domains (i) sensors and energy efficient wireless communication protocol and (ii) intelligent data processing with machine learning techniques. While the solution holds great potential some research activities are still needed to test the set-up of such a system in an operational environment. We have state of the art sensing technology that can be applied everywhere but the anomaly detection methods demand fine-tuning on real data from the operational environment. Within this POC we will start a trajectory to gain insights into the breadth of the use and market potential for the technology and get an accurate picture of the technological and contextual requirements that can enhance its adoption by a technology provider that scales it up to the port. Market niches opportunities in the short and long term will be identified by working in close collaboration with the port and its chainport network (e.g. Ports of Rotterdam, Hamburg and LA).

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Latré Steven
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Data relevant for passerby scanning is collected in silos and/or with a specific use case in mind. There is no technical solution for merging that data nor for assessing mobility comprehensively. The current state of the art in this domain is that - Data brokers exist, unlocking urban data and presenting it without further processing. - Data providers recognize the limits of the data they collect. - Individual partners have set up (2-way) collaborations with limited success. - Fusion of available data for one mobility view is not possible at this point. - Silo solutions (verticals) exist. - Academic studies using GPS traces abound, but are not acceptable in a GDPR-governed B2B market. In this project, we develop a framework for establishing a mobility data economy, which serves as a neutral playing field for brokering available mobility data. The goal is that it is fused for accurate assessments and as input for the next generation of mobility models. We take into account privacy & ethics, performance, validation of data and the needs of policy. The CityFlows platform, datasets, consortiums and stakeholders are essential components for enabling this novel business ecosystem.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The growing demand and the respective standardization for low power mass-market user equipment (UEs) in many industries including the positioning, navigation and timing (PNT) sector demands innovative methods and technologies from all stakeholders. OHB Digital Solutions (OHB DS) is active in the field of GNSS navigation and precise and robust positioning. Peopletrust (PT) has a consolidated expertise in the design and manufacturing of tracking/IoT solutions that include positioning functions. IMEC has a thorough understanding and expertise in terrestrial-based low power LPWAN localization algorithms. They have united in a consortium to address the BreadboardPos project using both their existing expertise and technologies as well as development of innovative tools. The BreadboardPos project aims at designing and building of breadboard UE demonstrators for innovative very low energy positioning concepts for mass-market IoT localisation. In particular, the following issues are the most relevant for the project: * Reviewing and characterizing the relevant modern GNSS-related, terrestrial- and space-based IoT low energy user positioning techniques. Selecting the most promising available technologies. * Defining the relevant use cases for ultra-low power positioning (with battery lifetime up to several years). Selecting benchmark routines and defining figures of merit to assess the performance of developed UE breadboards. * Designing and implementing breadboards demonstrators integrating the selected low power positioning techniques. * Assessing the breadboards performance in the terms of power consumption according to defined benchmarks The consortium has received letters from manufacturers such as u-blox, Rock Seven, Telespazio, ORBCOMM, Lacuna Space, and CLS Argos/Kinéis in support of the project. They agree to provide the tenders with technical information and support, facilitate the procurement of hardware modules (when deemed desirable), and grant access to satellite network capacity (when supported by the manufacturer) in order to facilitate project activities for testing and analysis. These services will be offered to the tenderers free of charge or at favourable commercial conditions. The activities and the developed technologies in this project will contribute to each respective partner's roadmap by bringing specialized know-how in addition to establishing a path for further expansions. Dissemination of results to the scientific and engineering community at large will also foster more progress in these areas.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Recent developments in 5G IC modems are expected to become available in the market this year, enabling new applications. Edge AI and 5G are globally considered ideal to enable new applications of massive sensor nodes, massive data, low latency and high reliability with local AI data handling. This project addresses the challenges associated with this: 5G gateway & Edge AI board design, EMI/EMC challenges, higher heat dissipation, modem parameterization for optimal performance in latency, bandwidth and low energy, Interoperability with devices from different network vendors, dynamic A.I. architecture with distributed processing between EDGE & CLOUD/SERVER

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Currently, most AI systems are being run in cloud environments. For some systems, like real-time systems, this can be troublesome, and moving these AI algorithms to the edge can provide a solution to these problems. The aim of my research is to use reinforcement learning techniques to design neural networks with performance rivalling that of modern, state-of-the-art systems, while reducing the resource consumption of these systems to a level that is manageable for edge devices. In order to achieve this goal, my work is split into 3 large components: multi-objective optimization, hardware embeddings and model-based reinforcement learning (MBRL) using monte carlo tree search (MCTS). The first component of my research, will deal with the scalarization of a multi-objective reward function, into a scalar reward. This is necessary for reinforcement learning systems, since they take a single reward value as feedback. For the second component of my research I will try to find a way to represent a certain piece of hardware, in a neural-network friendly manner. This is necessary for our system to be able to be able to exploit the architectural features of a specific piece hardware. Finally, I will introduce MBRL using MCTS to the field of neural architecture search. In this component, I will utilize the developed scalarization techniques and hardware representation from the first two components and a MBRL system to generate neural network architectures targeted at specific devices.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Mercelis Siegfried
• Fellow: Cassimon Amber

Research team(s)

• Internet Data Lab (IDLab)
• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

Reinforcement learning is an active field in machine learning where an agent learns to perform a task by interacting with the environment and receiving positive or negative rewards depending on the chosen actions. Recently, reinforcement learning has seen some big breakthroughs in beating the best human players various tasks, such as the classic board game Go and the popular video game StarCraft II. One of the reasons why the architectures that were used are so successful is that deep learning modules are used which can perform some form of relational reasoning. This allows them to view the environment in terms of distinct objects and make use of the relations between these objects. The field of relational reinforcement learning looks at how these relations between objects can be learned and used to optimally solve the given tasks. As this field is relatively new, there are still many open research questions, such as how to best create a representation of the environment based on these objects and relations and how to improve the efficiency of these networks by learning only the important relations while ignoring the irrelevant ones. In this proposal we introduce new relational reinforcement learning architectures that will allow us to efficiently represent the environment in a relational way, improve efficiency by focusing on the important relations in this representation and increase the ability to generalize to unseen tasks.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Avé Thomas

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Internet of Things (IoT) vision has enabled the wireless connection of billions of battery-powered devices to the Internet. However, batteries are expensive, bulky, cause pollution and degrade after a few years. Replacing and disposing of billions of dead batteries every year is costly and unsustainable. We posit the vision of a sustainable Internet of Battery-Less Things (IoBaLeT). We imagine battery-less devices storing small amounts of energy in capacitors, harvested from their environment or obtained through simultaneous wireless information and power transfer (SWIPT). Using this energy, these intermittently-powered devices are able to cooperatively perform sensing, actuation and communication tasks. Existing battery-less technology has many shortcomings. Such devices, usually based on passive RFID and backscatter, only support simple sensing, unable to handle more complex application logic. Networks do not scale, have a short range and a very low throughput. The goal of IoBaLeT is to bring battery-less technology to the next level. We envision battery-less devices and networks that support complex sensing and actuation applications, and offer throughput, scalability and range on-par with their battery-powered counterparts. To achieve this, we propose a novel battery-less IoT device design that relies on a combination of SWIPT, hybrid energy harvesting, active transmissions and wake-up radios. The project will innovate in terms of SWIPT efficiency, battery-less networking protocols, and distributed intermittent computing paradigms and scheduling algorithms. Leaving batteries behind will enable IoT applications at an unprecedented scale, with a significantly extended lifetime and in hard-to-reach places.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Delgado Carmen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Internet of Things (IoT) vision has enabled the wireless connection of billions of battery-powered devices to the Internet. However, batteries are expensive, bulky, cause pollution and degrade after a few years. Replacing and disposing of billions of dead batteries every year is costly and unsustainable. We posit the vision of a sustainable Internet of Battery-Less Things (IoBaLeT). We imagine battery-less devices storing small amounts of energy in capacitors, harvested from their environment or obtained through simultaneous wireless information and power transfer (SWIPT). Using this energy, these intermittently-powered devices are able to cooperatively perform sensing, actuation and communication tasks. Existing battery-less technology has many shortcomings. Such devices, usually based on passive RFID and backscatter, only support simple sensing, unable to handle more complex application logic. Networks do not scale, have a short range and a very low throughput. The goal of IoBaLeT is to bring battery-less technology to the next level. We envision battery-less devices and networks that support complex sensing and actuation applications, and offer throughput, scalability and range on-par with their battery-powered counterparts. To achieve this, we propose a novel battery-less IoT device design that relies on a combination of SWIPT, hybrid energy harvesting, active transmissions and wake-up radios. The project will innovate in terms of SWIPT efficiency, battery-less networking protocols, and distributed intermittent computing paradigms and scheduling algorithms. Leaving batteries behind will enable IoT applications at an unprecedented scale, with a significantly extended lifetime and in hard-to-reach places.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Mercelis Siegfried

Research team(s)

• Modelling for Sustainability (M4S)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The aim of the DAP²CHEM project is to facilitate the transition of chemical / pharmaceutical companies to industry 4.0, i.e. the integration of digital technologies and automation in production & logistics and the use of Industrial Internet or Things (IIoT) ", data analysis and digitized services in industrial processes. This goal is being pursued by proving technological proof-of-concepts for three test cases of three chemical / pharmaceutical companies and on the other hand by creating, demonstrating and sharing these success stories and "best practices" with others businesses. More specifically, the DAP²CHEM project generates the necessary generic knowledge for real-time data usage with using "Artificial Intelligence (AI)" systems for improved process development, optimization and production excellence in the (chemical / pharmaceutical) process industry.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Venue mapping is a special case of the reverse geocoding problem. Given user's GPS coordinates, an accuracy radius and a list of venues located inside that radius, we want to derive which venue did the user visit. Unfortunately, noise in the signal, and especially in dense urban areas, limits our ability to achieve satisfactory results. Resent research shows that it is possible to improve the results by incorporating temporal and behavioral knowledge into the venue mapping model. As a company specializing in analyzing sensor data, such as accelerometer, gyroscope and GPS, from mobile devices, Sentiance has a vast amount of data for thousands of users. An open question is how to represent the data so that the model could be trained in fully data-driven fashion. Manually creating rules or labelling millions of venues is not an option and would not result in a scalable, future-proof solution. Restricted by the lack of labelled data, we studied the latest achievements in Deep self-supervised learning in order to design a model that would be able to autonomously reveal the internal patterns available in the unlabeled data. In order to guarantee rich generalization capabilities of our model, we searched for ways to incorporate more knowledge into our model by means of publicly available data and Transfer learning. Despite the fact that such datasets exist, we faced another problem – the format of the data is so different from our in-house data, that none of the existing Transfer Learning techniques could be applied directly. Finally, to tackle this challenge we studied the fields of Multimodal learning and Multi-task learning. In this project we propose training a series of Deep learning models with a novel architecture that would result in a new state-of-the-art solution for the venue mapping problem.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Latré Steven
• Fellow: Musaev Gadzhi

Research team(s)

• Internet Data Lab (IDLab)
• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

The overall objective of 5G-Blueprint is to design and validate a technical architecture, business and governance model for uninterrupted cross-border teleoperated transport based on 5G connectivity. 5G-Blueprint will explore and define: - The economics of 5G tools in cross border transport & logistics as well as passenger transport: bringing CAPEX and OPEX into view, both on the supply (Telecom) side and on the demand (Transport & Logistics) side for transformation of current business practices as well as new value propositions - The Governance issues and solutions pertaining to responsibilities and accountability within the value chain dependent on cross border connectivity and seamless services relating to the Dutch & Belgian regulatory framework (telecommunications, traffic and CAM experimentation laws, contracts, value chain management) - Tactical and operational (pre-) conditions that need to be in place to get full value of 5G tooled transport & logistics. This includes implementing use cases that increase cooperative awareness to guarantee safe and responsible tele-operated transport - Preparing and piloting tele-operated and tele-monitored transport on roadways and waterways to alleviate the increasing shortage of manpower and bring transport and logistics on a higher level of efficiency through data sharing in the supply chain and use of AI. - Exploring the possibilities of increasing the volume of freight being transported during the night where excess physical infrastructure capacity is abundant; the lowering of personnel costs would make this feasible on a cost effective basis - Tele-operation will be enabled by the following 5G qualities, such as low latency, reliable connectivity and high bandwidth that current 4G LTE cannot deliver sufficiently. The project's outcome will be the blueprint for subsequent operational pan-European deployment of teleoperated transport solutions in the logistics sector and beyond.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

The overall objective of 5G-Blueprint is to design and validate a technical architecture, business and governance model for uninterrupted cross-border teleoperated transport based on 5G connectivity. 5G-Blueprint will explore and define: - The economics of 5G tools in cross border transport & logistics as well as passenger transport: bringing CAPEX and OPEX into view, both on the supply (Telecom) side and on the demand (Transport & Logistics) side for transformation of current business practices as well as new value propositions - The Governance issues and solutions pertaining to responsibilities and accountability within the value chain dependent on cross border connectivity and seamless services relating to the Dutch & Belgian regulatory framework (telecommunications, traffic and CAM experimentation laws, contracts, value chain management) - Tactical and operational (pre-) conditions that need to be in place to get full value of 5G tooled transport & logistics. This includes implementing use cases that increase cooperative awareness to guarantee safe and responsible tele-operated transport - Preparing and piloting tele-operated and tele-monitored transport on roadways and waterways to alleviate the increasing shortage of manpower and bring transport and logistics on a higher level of efficiency through data sharing in the supply chain and use of AI. - Exploring the possibilities of increasing the volume of freight being transported during the night where excess physical infrastructure capacity is abundant; the lowering of personnel costs would make this feasible on a cost effective basis - Tele-operation will be enabled by the following 5G qualities, such as low latency, reliable connectivity and high bandwidth that current 4G LTE cannot deliver sufficiently. The project's outcome will be the blueprint for subsequent operational pan-European deployment of teleoperated transport solutions in the logistics sector and beyond.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)
• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

In places where crowds gather, it is especially important for event organisers to be able to make an accurate estimate of the number of people present. In order to invest in a particular method, a fair comparison of the counting methods is necessary.  Our earlier research strongly pointed to the need for calibration of different counting methods and a pooling and exploitation of knowledge and expertise among these organisers in order to help them professionalise and enable (further) growth. The project will result in a better understanding of technologies for visitor counting through a decision tree based on a fair comparison of (1) the number of attendees at a given time and (2) the number of unique visitors during the event which also provides guidelines for extrapolating the counts. The decision tree will result in, among other things, more accurate predictions, impact analyses, deployment of resources and a better choice of visitor counts based on accuracy.  Results Context and objective Mapping visitor numbers at events has become more important than ever since the corona crisis. Having a clear view of how many visitors are present at a venue is the basis of crowd management. However, measuring crowds is challenging. Organisers, security personnel, security forces and other stakeholders often talk about varying visitor numbers at the same event. Technological counting methods also contradict each other. The need for calibrations for different counting methods is high. This project systematically checked the accuracy of different counting methods. More specifically, this project investigated the employability and accuracy of manual click and quadrant counts, as well as that of four technological counting methods commonly used at events: camera counting, Wi-Fi counting, mobile data counting and radio wave counting. Test events Due to the corona crisis, the events sector went on lockdown for a long time and events could not take place at various times during the course of project. When the sector was allowed to restart, it was first in the form of test events that required government approval. In the next phase, events could go ahead subject to compliance with a limited maximum capacity. Since the summer, the deployment of a Covid Safe Ticket (CST) ensured that events could once again proceed in as normal a manner as possible at full capacity. The research team, together with the various counting method providers, chose to pool their knowledge and expertise and deploy them to ensure a safe restart of the events sector. For this reason, we conducted measurements at test events, events with limited capacity as well as events that used a CST. Moreover, different types of events participated as test cases in this project, which also resulted in a lot of variation in terms of content. In this way, the difficult situation the events sector was in gave an extra dimension to this project and (often in consultation with the National Crisis Centre) we were able to support the events sector in difficult and uncertain times. Counting Guide The results of the research were compiled in a handy tool available on the website www.telwijzer.be. You can use the Counting Guide to determine the most appropriate counting method(s) for your event.

Researcher(s)

• Promoter: Berkvens Rafael
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In order to understand biological processes such as migration, dispersal and disease transmission, we need to know where animals are moving and who they are meeting. While this has been achieved for a lot of larger animals, the vast majority of animals are too small to effectively monitor without compromising on data accuracy or acquisition rates. This has implications not only for research into animal movement and behaviour, but also for applied applications such as better welfare for captive animals and livestock, and environmental monitoring. The recent advances in the Internet of Things (IoT) which has revolutionized various aspects of daily life have enormous potential in the field of wildlife tracking, but as yet have been little exploited, particularly when considering miniaturized options. We developed ProxLogs, an integrated, flexible and accessible monitoring system for small animals, based around recent improvements to Bluetooth Low Energy protocols. This project aims to develop the Minimum Viable Product, test it in operational environments, and investigate the appropriate business model of the system. This will be a state-of-the-art system which will allow the monitoring of far smaller wild and domestic animals at a greatly improved spatiotemporal scale than has previously been achieved, all while ensuring the system remains low cost and accessible for end users through our use of the widely available Bluetooth protocols. In this project we will further validate the prototype and investigate different potential business models.

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Berkvens Rafael
• Co-promoter: Kirkpatrick Lucinda
• Co-promoter: Leirs Herwig

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Wireless sensor networks (WSNs) are a key enabler for Internet of Things (IoT) applications in many different industries such as logistics, healthcare, agriculture, smart homes and smart vehicles. The location of one or more of the devices (nodes) in the network is interesting if not crucial information for the application. For this reason, algorithms for the self-localization of wireless sensor networks have been developed which are capable of automatically determining the position of the static nodes relative to each other. However, these methods are not equipped to effectively define a distance estimate in complex environments. Moreover, path loss exponents should be adapted to each link between the nodes in particular for better results. We hypothesize that we can model both the location and signal attenuation of WSN nodes so that the individual communication link distances can be ascribed to node mobility or environmental changes by incorporating context information such as environment layout or likely node locations.

Researcher(s)

• Promoter: Berkvens Rafael

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Inland Waterborne Transport (IWT) advantages as low-energy and low CO2 emitting transport mode are not fully exploited today due to gaps in the logistics system. Inland container vessels pay 6-8 calls at seaport terminals with long waiting times. More time is lost by sub-optimal navigation on rivers and waiting at bridges and locks. In addition, low load factors of containers and vessels impact the logistics systems with unnecessary high numbers of containers being transported and trips being made. NOVIMOVE strategy is to "condense" the logistics system by improving container load factors and by reducing waiting times in seaports, by improved river voyage planning and execution, and by facilitating smooth passages through bridges and locks. NOVIMOVE's innovations are: (1) cargo reconstruction to raise container load factors, (2) mobile terminals feeding inland barges, (3) smart river navigation by merging satellite (Galileo) and real time river water depths data, (4) smooth passage through bridges/locks by dynamic scheduling system for better corridor management along the TEN-T Rhine-Alpine (R-A) route, (5) concepts for innovative vessels that can adapt to low water condition while maintaining a full payload, and (6) close cooperation with logistic stakeholders, ports and water authorities along the R-A route: Antwerp, Rotterdam, Duisburg, Basel. NOVIMOVE technology developments will be demonstrated by virtual simulation, scaled model tests and full-scale demonstrations. NOVIMOVE innovations will impact the quantity of freight moved by IWT along the R-A corridor by 30% with respect to 2010 baseline data. The NOVIMOVE 21-members consortium combines logistics operators, ports, system-developers and research organisations from 4 EU member states and two associate countries. The work plan contains 4 technical Work Packages. The project duration is four years; the requested funding is 8,9 MIO.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)
• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

Nanotechnology is paving the way toward nanodevices that will enable several groundbreaking healthcare applications. Nanodevices are expected to flow through the human body, perform actions at certain locations, and communicate monitoring results to the outside world. There is, therefore, a need to enable two-way communication between the nanodevices and the outside world, as well as their localization inside the body. These functionalities should be supported while simultaneously maintaining tiny form factors and a low energy consumption profile of a potentially vast number of nanodevices. In the ScaLeITN project, I will utilize wireless signals in the terahertz (THz) frequencies for enabling both localization and communication capabilities. Localization will be enabled through THz backscattering, which is an unexplored paradigm that promises low energy and high precision nanoscale localization. The constrained communication range characteristic for in-body propagation will be mitigated through multi-hopping, where only a selected subset of nanodevices in the multi-hop route will be awoken. Selection of relays will be based on their location estimates and energy lifecycle characterizations. This is again a novel paradigm that promises enabling low power and scalable nanocommunication. The main outcome is to develop a pioneering prototype of an in-body THz nanonetwork with both localization and two-way communication capabilities.

Researcher(s)

• Promoter: Famaey Jeroen
• Fellow: Lemic Filip

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Interpretation and explanation of deep models is critical towards wide adoption of systems that rely on them. Model interpretation consists on getting an insight on the information learned by a model from a set of examples. Model explanation focuses on justifying the predictions made by a model on a given input. While there a is a continuously increasing amount of efforts addressing the task of model explanation, its interpretation counterpart has received significant less attention. In this project we aim taking a solid step forward on the interpretation and understanding of deep neural networks. More specifically, we will focus our efforts on four complementary directions. First, by reducing the computational costs of model interpretation algorithms and by improving the clarity of the visualizations they produce. Second, by developing interpretation algorithms that are capable of discovering complex structures encoded in the models to be interpreted. Third, by developing algorithms to produce multimodal interpretations based on different types of data such as images and text. Fourth, by proposing an evaluation protocol to objectively assess the performance of algorithms for model interpretation. As a result, we aim to propose a set of principles and foundations that can be followed to improve the understanding of any existing or future deep complex model.

Researcher(s)

• Promoter: Oramas Mogrovejo José Antonio
• Fellow: Behzadi Khormouji Hamed

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Recycl.AI is studying the use of deep learning in the circular economy. This project will start from an existing deep-learning based computer vision algorithm, which has been developed in the past by IDLab. The algorithm is able to predict the product category of e-waste.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The latest generation of WiFi technology, known as mmWave WiFi, utilizes comparatively higher frequencies than traditional WiFi. To combat high signal attenuation at mmWave frequencies, mmWave WiFi utilizes directional transmission and reception of signals. By utilizing directional communication at high frequencies, and in contrast to omni-directional and low-frequency traditional WiFi technologies, mmWave WiFi can deliver tens of gigabits per second bitrates required by various ground-breaking applications (e.g., virtual reality and aerial wireless networks). To establish strong communication links, in mmWave WiFi the directions of transmit and receive beams must be properly aligned, which is a process known as beam-steering. The current beam-steering mechanisms do not perform well under in dynamic conditions, i.e., when the communicating devices are mobile or if there are humans obstructing the communication. Therefore, there is a need for developing new beam-steering mechanisms that will be able to mitigate these negative effects. Consequently, experimental evaluation of these newly developed mechanisms will be required in order to benchmark their performance against the existing ones. To guarantee fair comparative benchmarking, there is a need for highly repeatable experimentation, i.e., different instances of an experiment must be performed in a way that preserves all experimental conditions (apart from exchanging the beam-steering mechanisms), pertaining primarily to the repeatable mobility patterns of the communicating devices, as well as the mobility patterns of humans causing obstacles. Such conditions cannot be achieved if humans are involved in the experimentation, either as carriers of devices or as obstacle generating factor. To alleviate these issues, we will develop a testbed infrastructure for fully repeatable mmWave WiFi experimentation with device mobility and moving obstacles. The repeatability will be guaranteed by utilizing drones as the carriers of mmWave WiFi devices and a combination of a robotic mobility platform and mannequin resembling a moving human-like obstacle. Once developed, this testbed infrastructure will increase the visibility of our university to a large heterogeneous audience and allow as to kick-start our research activities in the highly-promising mmWave WiFi domain. In addition, the testbed will be convenient for a broad range of experimentation with mobile wireless infrastructures going beyond the scope of the initially envisioned beam-steering in mmWave WiFi experimentation.

Researcher(s)

• Promoter: Lemic Filip

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The ever-increasing need for real-time communication of factory processes in one hand, and the offered flexibility of wireless communication on the other, is pushing Time Sensitive Networking (TSN) evolution towards the wireless networks. By definition, wireless networks are non-deterministic due to their random channel access mechanism. In order to introduce TSN vision to the wireless world, such randomness needs to be controlled. In this project, we develop a complete end-to-end system that enables and controls TSN in both wireless and wired domain.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Interaction between humans and artificial intelligence (AI) is still lacking the degree of engagement and entrainment that characterizes interaction between humans. The project WithMe aims at bridging this gap by investigating in detail the processes that happen in the human brain when engaged in an activity together with an other person: pursuing a common goal or simply enjoying a common activity. The brain signals that will be explored are characteristic for attention, emotion, and reward. Based on findings with people, WithMe will investigate the characteristics of an audiovisual interaction that makes it feel human, but the project will also explore whether an AI system could benefit from direct access to biomonitoring of the person it is communicating with. The new human-AI approach thus derived will open a wealth of new applications in health, revalidation, communication and information sharing, entertainment, etc.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Open city - Knowledge transfer with respect to the disclosure of standards and best practices for an open 'datafied' society within the domain of environmental parameters (air quality, sound, water, light, electromagnetic exposure...) as a foundation for innovative value models.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Today, drones are sophisticated observers. They can capture data more efficiently than traditional alternatives. They can also significantly reduce risks associated with specific observations, eliminating the need for humans to be physically present in hazardous environments. The drones of tomorrow will evolve from mere observers to highly automated, autonomously operating and even decision-making tools. The sky's the limit for the applied science of flying robots – or "dronebots". in this project, we investigate how a low power radar can be integrated with low power neural network models to perform accurate obstacle avoidance and drone navigation.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

When you want to go somewhere, you first need to know where you are. Autonomous driving or flying requires real-time, reliable and accurate location information. We explored if Ultra-Wide Band (UWB) communication can provide this information in dynamic environments. In this project, we applied Monte Carlo sampled Bayesian filtering to create robust measurement models embedded on the autonomous platform. We find that we can achieve real-time cm-level accuracies in a warehouse lab setup. These filters will enable autonomous operation in warehouses of the future.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Internet of Things (IoT) is largely powered by batteries. This poses significant challenges for its sustainability and longevity, as batteries are short-lived, bulky and polluting. To overcome this problem, we posit the vision of a battery-less IoT network, where devices are powered by energy harvesting and tiny long-lived capacitors. However, such devices often run out of power, resulting in intermittent on-off behavior. Traditional static sequential applications cannot handle such behavior, as they lose forward progress. This problem can be solved with task-based applications, consisting of a chain of interconnected tasks. Each task performs some atomic function, and its output is saved in non-volatile memory after it successfully completes. This allows the application to ensure forward progress in face of frequent power failures. Optimally scheduling the execution of such tasks, in face of the specific behavior of various energy harvesters, as well as the capacitor, and given extremely constrained resources of battery-less devices, is non-trivial. In this project, we propose a novel task scheduler that takes these aspects, as well as the deadline of tasks into account.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Delgado Carmen
• Co-promoter: Hellinckx Peter
• Co-promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project is part of the IMEC Frame Agreement and is being given as structural investment for fundamental research based on yearly set KPIs from the group to IMEC. This A-budget is defined within the IMEC Way of Working and part of the frame agreement of the University of Antwerp and IMEC.

Researcher(s)

• Promoter: Oramas Mogrovejo José Antonio

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Internet of Things (IoT) vision has enabled the wireless connection of billions of battery-powered devices to the Internet. However, batteries are expensive, bulky, cause pollution and degrade after a few years. Replacing and disposing of billions of dead batteries every year is costly and unsustainable. We posit the vision of a sustainable Internet of Battery-Less Things. We imagine battery-less devices storing small amounts of energy in capacitors, harvested from their environment. Using this energy, these intermittently-powered devices can cooperatively perform sensing, actuation and communication tasks. Existing battery-less technology has many shortcomings. Such devices, usually based on passive RFID and backscatter, only support simple sensing, unable to handle more complex application logic. The goal of this project is to bring battery-less technology to the next level. We envision battery-less devices and networks that support complex sensing and actuation applications. To achieve this, we will investigate a novel energy-aware task scheduler for intermittent devices that intelligently decides which application and network tasks to execute at which time, considering task deadlines, data freshness, expected energy consumption of interconnected tasks and available and expected harvested energy. To further improve performance, cooperative task scheduling extensions to support offloading of computing tasks to powered cloud edge devices will also be investigated.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

By making waterway transport more cost-efficient, Smart Waterway will enable a modal shift for last mile urban logistics from the road to the small waterways in many European cities, including a city as Ghent. For small barges that could enter these waterways, however, the cost of automating a vessel is high compared to the construction cost. Hence, a cost reduction in automating small vessels will be crucial in this shift. We believe this can only be reached by drastically reducing the equipment cost on the autonomous vessel. This does, however, require significant advances in sensing and localization technology. Although a lower accuracy is sufficient for autonomous waypoint-based navigation, low-cost onboard sensors will not suffice in more complex scenarios (i.e. locks, bottlenecks such as bridges, loading and unloading bays) where accurate localization is needed to safely maneuver the vessel. To overcome this issue, these critical locations will be equipped with additional sensors (e.g., IR, cameras) and a novel ultra-wideband localization system. By combining low-cost onboard sensors with infrastructure near critical locations, Smart Waterway aims to achieve economically viable level 3 autonomy in urban waterways.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Latré Steven
• Co-promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Many real-world applications require intelligent cooperative agents that can work together to solve a problem. An example of such an cooperative multi-agent application is the control of multiple autonomous vehicles. Multi-agent reinforcement learning is a wellresearched topic and many solutions exist in the state-of-the-art. Recently, the research community was able to create agents that learn how to communicate with each other to reach their goal. This is a new subfield of the multi-agent reinforcement learning domain in which we will research how we can achieve decentralised training of these communicating agents. This will allow us to create heterogeneous agents that can communicate or continue to train the communicating agents after their deployment which is not possible with the current state-of-the-art methods. In this project, I will extend the state-of-the-art by investigation how we can communicate with an unknown number of other agents which is a problem with state-ofthe- art methods. Next, I will work on the feedback structure that is used to train the communication between the agents. After the feedback structure I will work on splitting the agents architecture to create environment specific agents. I hypothesise that this will decrease the training time of the communication policy which is required for decentralised training. These advancements will be combined to create agents that we can train in a decentralised setting.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Mercelis Siegfried
• Fellow: Vanneste Simon

Research team(s)

• Modelling for Sustainability (M4S)
• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Large music festivals such as Studio Brussels' 'De Warmste Week' and Tomorrowland have a need for privacy conscious systems and algorithms that measure and analyse the density and flow of crowds for safety and security purposes. Such systems and algorithms for automated crowd status (density, flow, activity) assessments can alleviate the currently difficult task presented to trained staff and police forces. Our research group was the first to attempt to estimate large numbers of people through passive, radio frequency device free localization, at a venue supporting at least seven thousand attendees at the Tomorrowland music festival. Our preliminary data correlates with the rough density estimates currently available to event organisers, mainly coming from expert opinion based on a limited amount of cameras. My research will focus on three crucial aspects, which I will elaborate upon in this proposal: first, accurately model the relationship between crowd density and radio frequency signal features; second, estimate crowd flows in the environment; and finally, investigate if it is possible to relate crowd activity to the radio frequency signal features. By using radio signal features, the proposed system respects the privacy of individuals by design, making it truly non-intrusive.

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Berkvens Rafael
• Fellow: Kaya Abdil

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Automatic crowd density estimation can be highly useful for a multitude of applications, examples of which are traffic control, gauging interest at a trade fair and crowd control systems during large-scale events. Classic camera-based setups have several shortcomings, the most notorious of which is the potential for privacyrelated issues to occur. The use of a passive crowd estimator which makes use of an RF-based wireless sensor network (WSN) could provide a solution to this problem. A series of experiments which we performed by installing WSNs in large-scale music festival environments containing thousands of individuals indicated that the influence of the crowd on radio frequency communication within these networks can be used to obtain accurate crowd size estimates. In this project, we seek to validate this core principle for different types and sizes of environments. Furthermore, we wish to investigate how the environment type is related to the network size and the amount of training that is required to obtain accurate results. Finally, an in-depth analysis regarding the crowd density within subregions of these environments and the potential for this approach to allow for crowd flows to be determined, will be investigated as well. Furthermore, to commercialize this proof-of-concept, a go-to-market strategy will be further finetuned. This includes the identification of the different application sectors and to address the different benefits for customers.

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Berkvens Rafael

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The specific objectives of the SSAVE project are:   - Defining methods and technologies for secure and verified connectivity and sharing data between assets from different manufacturers and owners, with line-of-sight broadband communication capabilities over at least 1 km. - Defining low-cost IP-like technologies that allow the deployment of a meshed ad-hoc edge and narrow-band shared internet access, to enable the use of MDTs. - Working towards a standard format for the exchange of sensor data, characterized by data reduction between field assets from different manufacturers and owners and data enrichment. - Define software architectures to fuse sensor data, thereby delivering optimal data flows, with a focus on real-time sharing of situational awareness. - Standardization of inter-asset communication.   The ultimate goal is to enable different forms of autonomy in the sea and inland waterways.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Availability of water resources is under stress due to climatic changes, visible in the recent period of prolonged drought in Belgium, but also elsewhere. However, each year more than 60 mio m3drinking water is lost in Flanders. Leak localization today is very time consuming and labor intensive as operators have to manually place equipment that 'listens' to the water flow during the night. SmartWaterGrid will substantiate, facilitate and automate leak localization to respond more quickly to detected leaks by using innovative modelling to significantly reduce the number of costly sensors needed. To do so, hybrid digital twins of real-time flow and pressure measurements will be augmented with GIS data, physical models, and human feedback from customers and experts. This way, leak localization can be brought from +/- 70km, and weeks to months to exactly localize a leak, up to a soft real-time solution of less than 1 km (street level). Additionally, optimal operating parameters of the wireless sensor network will be determined to minimize battery energy consumption while feeding sufficient data into the digital twin. The type of field service order and required field service skills will also be determined to effectively resolve problems for end customers.

Researcher(s)

• Promoter: Berkvens Rafael
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Markov processes have found widespread use in the analysis of computer systems and beyond. Over time the size of the systems under consideration has grown considerably, e.g., Google has hundreds of thousands of servers located in its various data centers. This growth in the system size has made conventional methods to analyse these Markov processes infeasible. As such, deterministic approximations, also known as mean field or fluid models, have been introduced to analyse such large scale systems. Interestingly, these deterministic models have been shown to correspond to the limit of a sequence of appropriately scaled Markov processes showing that the systems behaviour becomes deterministic as the system size tends to infinity. These Markov processes typically have a countable state space and the limiting system is described by a set of ordinary differential equations. However, in order to analyse large scale computer systems with general job size distributions, one needs to keep track of the age or residual service time of each job. This makes the state space uncountable and the natural candidate for the limiting system becomes a set of partial differential equations (PDEs). The aim of this project is to develop PDE mean field models for large scale computer systems, to establish convergence results and to use these models to gain insight into the system behaviour. The project combines techniques from stochastic modelling, probability, numerical analysis and simulation.

Researcher(s)

• Promoter: Van Houdt Benny
• Fellow: Hellemans Tim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Emerging artificial intelligence applications in professional sports based on the use of sensors, wearables and video data offer huge opportunities to innovate live sports reporting, but the code to turn these data sources into attractive and meaningful stories has not yet been cracked. To unleash this potential, DAIQUIRI will develop a media-focused sensor data platform and professional dashboard allowing content creators to augment live sports experiences. DAIQUIRI targets both real-time augmentation of live TV and near-live story snippet inserts in an interactive set-top-box application layer. The project demonstrator will showcase end-to-end sensor data integration for reporting of hockey and cyclocross.  DAIQUIRI will address the current challenges of data tsunami handling,  sensor-video matching and enrichment, insights-driven captation and enable the automated orchestration of multi-modal story snippets through editorial AI algorithms. The consortium covers the full value chain bringing together unique expertise in sports event capturing (Videohouse, NEP), enriched sensor data platform (InTheRace, Arinti, imec-IDLab), editorial tooling and storytelling (VRT) and interactive user experiences in a living room environment (Telenet, imec-MICT).

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

InterConnect envisages to contribute for the democratization of efficient energy management, through a flexible and interoperable ecosystem where demand side flexibility can be soundly integrated with effective benefits to end-users. In fact, over the last few years several projects and technology providers have come up with solutions that allow every energy user to have awareness and control over his appliances, but there has always been a major issue with interoperability. End-users should be able to choose and change their technology providers, without having to replace their installation, every time they feel this need and still be able to adopt sustainable behaviour and benefit from technological advances. In the energy sector, a steep move towards digital is occurring and becoming tremendously user-centric and market-driven. The system dimension is significant, as the number of energy service providers is increasing thanks to favourable regulatory environment and technology advancements for monitoring and control. This is the reason why this consortium integrates relevant partners from all the representative stakeholders in this new energy paradigm. Specific competences in ICT, IoT, energy, data science, software, were included and the full value chain, from R&D institutions, manufacturers, DSO, retailers, IT providers, and energy users is represented. To guarantee a higher Europe-wide impact, several relevant associations related with ICT and energy are also involved. To achieve a significant dimension, 7 large scale pilots, in different countries and with different end-users, are foreseen to guarantee representativeness and dimension in terms of number of appliances and services. The overarching objective of these pilots is to demonstrate a real digital market environment over electrical systems with significant amounts of DSF, reducing operational and investment costs that will benefit energy end-users and help EU achieve its energy efficiency objectives.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of the HAI-SCS (Helicus Aero Initiative – Scheduling Connectivity Security) project is to enable complete, secure and safe automation of mission-critical UAV flights focusing on medical transport. To enable the above described objectives, important new technological innovations are planned within the HAI consortium SCS project: * An automated flight planning and scheduling algorithm able to learn in real time the best flight plan and schedule, given a high dimensional set of input parameters and multi-modal output options (flights, ground transport). Given the high dimensionality of the problem, the aim is to reinforce a learning-based approach, where the total reward of all UAS flights is maximized. A phased approach is being proposed in which a fixed corridor-based airspace design is being assumed in a first phase, allowing an operational handshake-based flight approval process that can be set up with the authorities. The second phase involves the inclusion of a flexible airspace design model in the flight planning and scheduling process. * A dynamic heterogenous quality of service (QoS) management layer able to provide seamless QoS across multiple communication channels (e.g., 5G, 4G, private networks, direct C-band links, etc.). Moreover, guaranteed connectivity, meeting the QoS requirements, need to be constructed and scaled up/down instantly. * A versatile security management system that provides the building blocks to secure the communication and control of the UAS taking into account the specific security and performance requirements of the application (e.g., low latency, high bandwidth) and the resource constraints of the UAS (e.g., battery capacity).

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of the HAI-SCS (Helicus Aero Initiative – Scheduling Connectivity Security) project is to enable complete, secure and safe automation of mission-critical UAV flights focusing on medical transport. To enable the above described objectives, important new technological innovations are planned within the HAI consortium SCS project: * An automated flight planning and scheduling algorithm able to learn in real time the best flight plan and schedule, given a high dimensional set of input parameters and multi-modal output options (flights, ground transport). Given the high dimensionality of the problem, the aim is to reinforce a learning-based approach, where the total reward of all UAS flights is maximized. A phased approach is being proposed in which a fixed corridor-based airspace design is being assumed in a first phase, allowing an operational handshake-based flight approval process that can be set up with the authorities. The second phase involves the inclusion of a flexible airspace design model in the flight planning and scheduling process. * A dynamic heterogenous quality of service (QoS) management layer able to provide seamless QoS across multiple communication channels (e.g., 5G, 4G, private networks, direct C-band links, etc.). Moreover, guaranteed connectivity, meeting the QoS requirements, need to be constructed and scaled up/down instantly. * A versatile security management system that provides the building blocks to secure the communication and control of the UAS taking into account the specific security and performance requirements of the application (e.g., low latency, high bandwidth) and the resource constraints of the UAS (e.g., battery capacity).

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Flemish AI research program aims to stimulate strategic basic research focusing on AI at the different Flemish universities and knowledge institutes. This research must be applicable and relevant for the Flemish industry. Concretely, 4 grand challenges 1. Help to make complex decisions: focusses on the complex decision-making despite the potential presence of wrongful or missing information in the datasets. 2. Extract and process information at the edge: focusses on the use of AI systems at the edge instead of in the cloud through the integration of software and hardware and the development of algorithms that require less power and other resources. 3. Interact autonomously with other decision-making entities: focusses on the collaboration between different autonomous AI systems. 4. Communicate and collaborate seamlessly with humans: focusses on the natural interaction between humans and AI systems and the development of AI systems that can understand complex environments and can apply human-like reasoning.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Calders Toon
• Co-promoter: Daelemans Walter
• Co-promoter: Goethals Bart
• Co-promoter: Hellinckx Peter
• Co-promoter: Laukens Kris
• Co-promoter: Martens David
• Co-promoter: Sijbers Jan
• Co-promoter: Steckel Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Climate change will affect Arctic ecosystems more than any other ecosystem worldwide, with temperature increases expected up to 4-6°C. While this is threatening the integrity and biodiversity of the ecosystems in itself, the larger ecosystem feedbacks triggered by this change are even more worrisome. During millions of years, atmospheric carbon has been stored in the Arctic soils. With warming, the carbon can rapidly escape the soils in the form of CO2 and (even worse) the strong greenhouse agent CH4. Despite decades of research, scientists still struggle to unveil the scale of this carbon exchange, and especially how it will interact with climate change. An overarching question remains: how much carbon will potentially escape the Arctic in the future climate, and how will this affect climate change? FutureArctic embeds this research challenge directly in an inter-sectoral training initiative for early stage researchers, that aims to form "ecosystem-of-things" scientists and engineers at the ForHot site. The FORHOT site in Iceland offers a geothermally controlled soil temperature warming gradient, to study how Arctic ecosystem processes are affected by temperature increases as expected through climate change.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Climate change will affect Arctic ecosystems more than any other ecosystem worldwide, with temperature increases expected up to 4-6°C. While this is threatening the integrity and biodiversity of the ecosystems in itself, the larger ecosystem feedbacks triggered by this change are even more worrisome. During millions of years, atmospheric carbon has been stored in the Arctic soils. With warming, the carbon can rapidly escape the soils in the form of CO2 and (even worse) the strong greenhouse agent CH4. Despite decades of research, scientists still struggle to unveil the scale of this carbon exchange, and especially how it will interact with climate change. An overarching question remains: how much carbon will potentially escape the Arctic in the future climate, and how will this affect climate change? FutureArctic embeds this research challenge directly in an inter-sectoral training initiative for early stage researchers, that aims to form "ecosystem-of-things" scientists and engineers at the ForHot site. The FORHOT site in Iceland offers a geothermally controlled soil temperature warming gradient, to study how Arctic ecosystem processes are affected by temperature increases as expected through climate change.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Verdonck Tim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In this research project we want to take a further step in refining and concretising techniques for an automatic road surface quality inspection for application in the East Flemish Brabant region, in order to simplify road management and save costs by having the road surface at the right times at the right times. to renovate or renew places. The first results of the 3D Time-of-Flight camera with a wide image range, mounted on slow-moving vehicles (eg collection trucks), show great accuracy. To overcome the lack of in-depth measurement data with this technique, an extensive test case is proposed. Because this camera technology is quite expensive and the number of equipped collection trucks is more likely to be limited, this measurement method is combined with the cheap, but less accurate measurement method of the CANbus. This makes it possible to continuously generate data about the road surface quality via a fleet of wagons. The data obtained is then applied to the existing OCW inspection model (which is already being used by local authorities) and this model is further refined and aligned with the generated data. In the data processing of both measurement techniques, there is an eye for information security, open standards and data interoperability. Finally attention is paid to the integration of these results in a PMS (Pavement Management System). In the case of expansion, it is investigated whether data can be used for other purposes (eg registration and quality recording of lines, pedestrian and cycle paths, weed control). The integration of the continuous new and historical results in a PMS will make it possible to implement a proactive, substantiated policy and an efficient renovation with a simple result display (eg dashboard with map). After all, integration with the BBC would mean added value for every Flemish local government, so that citizens can ultimately enjoy a better maintained local road network.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Across Flanders, 2,500 small, energy-efficient and wireless sensors will continuously monitor the quality and quantity of water in Flanders in real time. The intention is to prevent flooding, scarcity and pollution. Researchers are developing a network of 2,500 sensors throughout Flanders, also known as the Internet of Water. They will monitor the quality and quantity of soil, ground and surface water and purified sewage water. These sensors will transmit the current measurement data permanently and in real time to an intelligent water management system. Sensors pass on real-time data to self-learning software, which in turn can make realistic predictions. That, in turn, enables us to take the correct measures in time. With Internet of Water, we provide our water managers with an innovative instrument that will enable them to better protect Flanders against flooding, scarcity or pollution.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Across Flanders, 2,500 small, energy-efficient and wireless sensors will continuously monitor the quality and quantity of water in Flanders in real time. The intention is to prevent flooding, scarcity and pollution. Researchers are developing a network of 2,500 sensors throughout Flanders, also known as the Internet of Water. They will monitor the quality and quantity of soil, ground and surface water and purified sewage water. These sensors will transmit the current measurement data permanently and in real time to an intelligent water management system. Sensors pass on real-time data to self-learning software, which in turn can make realistic predictions. That, in turn, enables us to take the correct measures in time. With Internet of Water, we provide our water managers with an innovative instrument that will enable them to better protect Flanders against flooding, scarcity or pollution.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

From 17 October 2019 to 19 April 2020, Kunstenstad is organizing the Cool Japan exhibition on the third floor of the MAS / Museum aan de Stroom. As part of the exhibition, the ""humanoid"" robot Pepper, owned by IDLab, will be on display. The intention is that he performs his humanoid functions and interacts live with visitors to the exhibition (young and old). IDLab rents out Pepper and will program it to start a conversation with visitors, host a quiz about the exhibition and do a dance.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The project aims to realize an autonomous management system for smart buildings that communicate based on the BLEv5 (Bluetooth Low Energy version 5) specification for mesh networks. The system includes (battery-free) devices that harvest energy, a mesh network that provides connectivity offers with the support of quality guarantees and can transfer energy wirelessly, and the connection to application-specific platforms in the Cloud. It will connect devices support them throughout their entire lifespan and thereby, in a demonstrable way, to their meet the required quality guarantees.

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Bikonnector is a proof-of-conceptproject. In this research project, we will focus on connected bikes for a better experience. The existing technology will be further examined with valorization as purpose.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In recent years, Low Power Wide Area Networks (LPWAN) have received much attention, due to the rise of the Internet of Things (IoT) and the need to localize devices in these long-range networks, using minimal power consumption. Asset tracking is one of the classic applications of LPWAN localization. However, the more accurate a localization algorithm, the more application potential (e.g. home automation, health care solutions and smart cities) there is to use this algorithm. Therefore, we need advanced technologies and algorithms to improve the accuracy and reliability of LPWAN localization. Although feature-based localization is widely used in indoor environments, we will extend the use of this methodology to outdoor environments. Features are defined as signal characteristics, such as signal strength. The class of feature-based localization can be subdivided into different subclasses. Fingerprinting and ranging are two of the most important techniques in the featurebased class. In this research, we will investigate new and existing algorithms to increase the accuracy and reliability of feature-based localization techniques in LPWAN. A comparative study between the accuracy and reliability of LPWAN technologies (Sigfox, LoRaWAN and NB-IoT) will be made as well.

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Berkvens Rafael
• Fellow: Janssen Thomas

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Since the introduction of the very first communication networks, queueing models have played a key role in improving network performance. This has resulted in a large body of queueing theory literature that has found widespread use in many other areas of science and technology. As the area of computer systems and networks is ever evolving, so is the need for new, tailored queueing models. Large-scale systems (e.g., grid computing or cloud computing) have become quite prevalent today and are often composed of many heterogeneous resources. The analysis of such large-scale heterogeneous systems using traditional queueing theory is prohibitively expensive as the required time and memory complexity tends to scale poorly in the system size. The aim of this project is to introduce and analyze new queueing models that provide insight into the performance of existing and novel load balancing and scheduling algorithms for large-scale systems. The problems under consideration include affinity scheduling problems motivated by MapReduce clusters, load balancers that make use of redundancy to mitigate latency caused by server unpredictability, and stateful load balancers. The main envisioned methodology exists in developing fluid approximations that are validated using simulation experiments and that can be shown to become exact as the system size tends to infinity. The project combines techniques from stochastic modelling, probability, dynamical systems, numerical analysis and simulation.

Researcher(s)

• Promoter: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Reinforcement Learning agents have attained incredible achievements over the past few years, with AlphaGo's resounding victory over one of the world's top Go players as a crowning achievement. A severe limitation of such agents is that they only know how to function in one very specific environment; AlphaGo is unable to play Go with a tweaked ruleset, let alone play competitively in a different board game. The meta-learning principle aims to improve this. By training the agent not only on one task, but instead on many tasks from a distribution, the trained agent can quickly learn how to behave in a novel task from the distribution. In this project, we propose several improvements to the field of meta-reinforcement learning. First, we propose a meta-learner based on Hierarchical Temporal Memory, which mimics the human brain according to our current understanding of it. This system adapts quickly to changing patterns in the environment—a desirable property for a meta-learner. We also investigate a plethora of ways to auto-generate these task distributions, and evaluate how we can introduce new abilities efficiently to an already trained meta-learner. Finally, we will extend a meta-learner to work with not just one, but with many task distributions. Ideally, such a system would be able to quickly learn to perform any conceivable task at least as well as a human.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Struye Jakob

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Everyone that makes use of wireless communication technologies such as Wi-Fi has definitely already experienced a badly performing network. Simply googling "better Wi-Fi performance" yields 64.000 results! Quite often this resulted in dissatisfaction and frustration, not only because of the bad performance itself, but also the inability to pinpoint the cause of the problem. Why aren't wireless networks sufficiently intelligent to optimise their configuration to the needs of the diverse applications on top? Looking at the latest evolutions, we see that these networks become increasingly flexible, exposing control capabilities that can be used to define how data must be handled. So, there is flexibility and the networks can be managed, but we argue that without rethinking the way how applications fit into network management, one will continue to perform configurations based on incomplete information. This way, suboptimal wireless network performance and user dissatisfaction will remain commonplace. Therefore, Ctrl-APP aims to establish a new networking paradigm, called appdaptive networking. This is achieved by extending the separation of data and control plane, a typical networking concept, to applications. This way, applications become able to pass intentions to the network, the networks can be properly instrumented to perform fine-grained diagnostics and the resulting knowledge can used to automatically learn and enforce the best configuration. - 1

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of this project is to introduce a simulation based methodology which will be used to cope with the scalability constraints of modern IoT software testing, and more specifically the testing of ultra large scale systems with emergent behavior. With IoT becoming more mainstream and with the rise in the amount of devices getting interconnected, the complexity and scale of the IoT landscape will largely increase. This interoperability between IoT devices and actuators of all sorts will prove to be vital for future IoT applications. As a result of the increased scale and diversity and because of modern decentralized IoT architectures such as Edge computing, we see that a whole new type of IoT application will gain importance. A type of application where local decentralized interaction between devices and actors will lead to a global emergent behavior. The concept of emergence can be compared to a flock of birds, where local interactions between individual birds lead to a global optimized behavior. This idea is also very relevant in IoT, imagine for example a smart traffic light application where local interactions between traffic lights could lead to a global optimized traffic flow. This type of IoT application will however lead to major difficulties with regards to application validation, testing and calibration. That is because in order for realistic emergent behavior to arise, the IoT application will need to be executed in a large-scale and diverse environment. An environment that resembles the eventual operational environment. Deploying such applications to a real-life isolated IoT testbed would be impractical as the cost of setting up such an environment at a realistic scale is too high and requires too much effort in the early stages of development. Instead of relying on expensive test beds, we propose a large scale simulation based approach. Such a simulation -based system needs to incorporate hundreds of thousands of virtual sensors interacting among each other and with the environment. The behavior of these systems will need to be modeled carefully. However, this leads to additional technical challenges. Also all virtual sensors in the system should be continuously active to interact in a real-time fashion with other systems. That is because an important part of the behavior of conventional IoT systems and EBI systems is controlled by an IoT middle-ware, the simulated entities should be able to interact with the middleware as if they were real-life IoT entities. We refer to this as software-in-the-loop (SIL) simulation. Because of this real-time requirement, a great amount of simulation entities should run in parallel which highly increases the computational complexity. Solely relying on state-of-the-art large-scale simulation techniques is insufficient. The contribution of this project is focused on the creation of a methodology for running real-time, large-scale simulations for testing and analyzing both conventional IoT systems and emergent behavior based IoT systems. We will focus on two major tracks, in the first we will reduce the computational complexity by dynamically increasing abstraction levels of simulation models and in the second track we aim at reducing network communication overhead of distributed simulations by optimizing the partitioning of simulation entities over multiple simulation servers.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Denil Joachim
• Co-promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

5G-MOBIX aims at executing CCAM trials along x-border and urban corridors using 5G core technological innovations to qualify the 5G infrastructure and evaluate its benefits in the CCAM context as well as defining deployment scenarios and identifying and responding to standardisation and spectrum gaps. 5G-MOBIX will first define the critical scenarios needing advanced connectivity provided by 5G, and the required features to enable those advanced CCAM use cases. The matching between the advanced CCAM use cases and the expected benefit of 5G will be tested during trials on 5G corridors in different EU countries as well as China and Korea. Those trials will allow running evaluation and impact assessments and defining also business impacts and cost/benefit analysis. As a result of these evaluations and also internation consultations with the public and industry stakeholders, 5G- MOBIX will propose views for new business opportunity for the 5G enabled CCAM and recommendations and options for the deployment. Also the 5G-MOBIX finding in term of technical requirements and operational conditions will allow to actively contribute to the standardisation and spectrum allocation activities. 5G-MOBIX will evaluate several CCAM use cases, advanced thanks to 5G next generation of Mobile Networks. Among the possible scenarios to be evaluated with the 5G technologies, 5G-MOBIX has raised the potential benefit of 5G with low reliable latency communication, enhanced mobile broadband, massive machine type communication and network slicing. Several automated mobility use cases are potential candidates to benefit and even more be enabled by the advanced features and performance of the 5G technologies, as for instance, but limited to: cooperative overtake, highway lane merging, truck platooning, valet parking, urban environment driving, road user detection, vehicle remote control, see through, HD map update, media & entertainment.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

5G-MOBIX aims at executing CCAM trials along x-border and urban corridors using 5G core technological innovations to qualify the 5G infrastructure and evaluate its benefits in the CCAM context as well as defining deployment scenarios and identifying and responding to standardisation and spectrum gaps. 5G-MOBIX will first define the critical scenarios needing advanced connectivity provided by 5G, and the required features to enable those advanced CCAM use cases. The matching between the advanced CCAM use cases and the expected benefit of 5G will be tested during trials on 5G corridors in different EU countries as well as China and Korea. Those trials will allow running evaluation and impact assessments and defining also business impacts and cost/benefit analysis. As a result of these evaluations and also internation consultations with the public and industry stakeholders, 5G- MOBIX will propose views for new business opportunity for the 5G enabled CCAM and recommendations and options for the deployment. Also the 5G-MOBIX finding in term of technical requirements and operational conditions will allow to actively contribute to the standardisation and spectrum allocation activities. 5G-MOBIX will evaluate several CCAM use cases, advanced thanks to 5G next generation of Mobile Networks. Among the possible scenarios to be evaluated with the 5G technologies, 5G-MOBIX has raised the potential benefit of 5G with low reliable latency communication, enhanced mobile broadband, massive machine type communication and network slicing. Several automated mobility use cases are potential candidates to benefit and even more be enabled by the advanced features and performance of the 5G technologies, as for instance, but limited to: cooperative overtake, highway lane merging, truck platooning, valet parking, urban environment driving, road user detection, vehicle remote control, see through, HD map update, media & entertainment.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Nowadays, predictive maintenance, remote monitoring of machinery, real-time communication between employees, etc. is considered essential for efficient operation and management of industrial environments. However, metal-containing ship & harbor environments such as ship compartments, remote control rooms of factories and individual stacked containers, still lack internet coverage, often due to the presence of large blocking metal const ruct ions. This challenge is tackled by the Internet of Shipping (IoS) project, which uses multi-hop sub-GHz wireless mesh network extensions together with locally available wired and wireless technology (e.g., satellite, Wi-Fi, PLC, Ethernet), to provide connectivity and positioning services in challenging shipping environments. The IoS connectivity services will be optimized for (i) safety operations (health monitoring, possibility to trigger alarms), (ii) track and tracing of goods, equipment and employees, (iii) communication between employees (voice calls from remote compartments & locations), and (iv) remote monitoring of machinery operations.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Berkvens Rafael

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Rare events in surveillance video streams are defined as events deviating from "normal" operation, and worth reporting or storing for later analysis. They can include cars driving unusually fast, fights on the street, people running away, unidentified objects, … Current state-of-the-art deep neural networks have achieved remarkable successes in terms of accuracy in a variety of AI-related tasks, but typically require a large set of labeled data to train to recognize specific objects and situations in a supervised way. This projects aims at investigating the use of deep neural networks to automatically and unsupervised learn the important features of a camera feed, and later report unusual events in the stream. IDLab-UA will investigate the applicability of 3rd generation neural networks (spiking neural networks) for this use case, and provide imec with insights on the potential of spiking neural networks (in comparison with quantized 2nd generation artificial neural networks). As an important aspect is the possibility of future on-chip learning the focus of the spiking neural networks research track is on using very simple models for spiking neurons, that would ease later implementation in dedicated hardware. The envisaged track is using temporal coding and one spike operational mode in a multi-layer (convolutional) network architecture. Training should be done using only local learning rules, and will start from standard STDP (Spike Timing Dependent Plasticity) and its variants. Further, specialized spiking response models, optimized for embedded inference with short term memory may be developed, and network size reductions techniques explored. The work on spiking neural networks will start from simple image classification (on the MNIST benchmark), provide an initial analysis of the use of dynamic vision sensor cameras (DVS cameras) for motion anomaly detection in video, and conclude with object and motion detection in short video fragments. Results will be compared with a research track using 2nd generation neural networks, executed by IDlab-Gent, and on the same data sets. Evaluation criteria will include the accuracy of the designed algorithms, the ability to reduce the number of false positive detections over time, the (possible) hardware footprint of the designed algorithms, and insights in the overall potential of spiking neural networks on data sets beyond the traditional simple benchmarks.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Many real-world applications require intelligent cooperative agents that can work together to solve a problem. An example of such an cooperative multi-agent application is the control of multiple autonomous vehicles. Multi-agent reinforcement learning is a well-researched topic and many solutions exist in the state-of-the-art. Recently, the research community was able to create agents that learn how to communicate with each other to reach their goal. This is a new subfield of the multi-agent reinforcement learning domain in which we will research how we can achieve decentralised training of these communicating agents. This will allow us to create heterogeneous agents that can communicate or continue to train the communicating agents after their deployment which is not possible with the current state-of-the-art methods. In this project, I will extend the state-of-the-art by investigation how we can communicate with an unknown number of other agents which is a problem with state-of-the-art methods. Next, I will work on the feedback structure that is used to train the communication between the agents. After the feedback structure I will work on splitting the agents architecture to create environment specific agents. I hypothesise that this will decrease the training time of the communication policy which is required for decentralised training. These advancements will be combined to create agents that we can train in a decentralised setting.

Researcher(s)

• Promoter: Hellinckx Peter
• Fellow: Vanneste Simon

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

imec will develop a detailed device classification model that extends the PoC sub-category classification model. They will then develop a device size classifier model. The next step in the project is the system development and integration, data engineering and reporting. This concludes phase 1 of the projects. In Phase 2, imec will develop a device classiciation model, detailed per device type.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Developing a sub-subcategory classification model that extends the PoC sub-category classification model. (i) Exploratory dataset analysis and dataset cleanup. (ii) Train new deep learning image classification models at sub-sub-category level (e.g., 02.01.a). (iii) Report on model performance.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Artificial Intelligence in general and deep learning specifically has experienced major breakthroughs in the last decade showing above human intelligence for complex tasks. Current deep learning technologies are however very power hungry and therefore require the use of large Graphical Processing Units (GPUs) farms in large-scale datacenters. With the recent advances in neural network hardware (neuromorphic computing design such as Neural Processing Units) we can expect more and more local neural networks being pushed to the edge of the network. However, what is lacking is exploiting the value of networking, and the Internet in particular, by connecting multiple heterogeneous learning systems together and allow more powerful learning-enabled applications to be built on top. The goal of this project is to create a layer which is able to connect multiple heterogeneous learning systems across the Internet so that they can act as a single deep learning system performing both on-line learning and inferencing. For this, we will develop both a low overhead communication protocol and Software Defined Networking-based control layer, which can define how and when different learning systems need to be connected. Finally, we will focus on the adaptation of various learning algorithms to this connected environment so that they are able to easily transfer knowledge from one learning system to the other.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Hutsebaut-Buysse Matthias
• Fellow: Rocco Rodolfo

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Health care is an essential service that uses a great deal of sensitive personal data which has a high black market value being a lucrative target for data theft and ransomware attacks.The EU NIS Directive (EU 2016/1148) and GDPR (EU 2016/679) will harmonize and improve information security in Europe. Both require relevant ICT infrastructure operators to perform risk assessments, introduce appropriate security measures to manage identified risks, and report security breaches. Unfortunately, risk-based approaches are notoriously difficult to implement in a consistent and comprehensive fashion. They depend on a high level of understanding of both cybersecurity and of the system or network to be protected, are labour intensive and costly and typically done by small teams. This is increasingly inappropriate as health care providers introduce IoT systems, cloud services and (in the near future) 5G networks to provide services in which patients are more engaged, may own some of the devices used, and want access in hospitals, on the move or at home. The ProTego project will develop a toolkit and guidelines to help health care systems users address cybersecurity risks in this new environment by introducing 3 main advances over current approaches: Extensive use of machine intelligence: a combination of machine inference exploiting a priory knowledge for security-by-design, and machine learning from data for run-time threat detection and diagnosis; Advanced data protection measures: advanced encryption techniques and hardware based full memory encryption, and multi-stakeholder IAM to control access to and by user devices, to protect data at rest and provide ultra-secure data exchange portals; Innovative protocols for stakeholder education: using security-by-design analysis to target training and support stakeholders to contribute to network overall security.The toolkit will be integrated and validated in IoT and BYOD-based case studies at two hospitals.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

5G for Connected and Automated Road Mobility in the European UnioN. The project will build a 5G-enabled corridor from Bologna to Munich to conduct cross-border trials of 5G technologies in three major use cases: vehicle manoeuvre negotiation (at various levels of automation), infotainment, and emission control. The 5G New Radio will be used to support latency sensitive and/or bandwidth hungry services and applications. The project will leverage on a distributed mobile edge cloud spanning from the vehicle itself to the centralised cloud. Multi-tenancy and neutral host concepts will be leveraged upon to deliver a final platform capable of enabling new business models. 5G-CARMEN will complement C-V2X with LTE and C-ITS technologies, targeting interoperability and harnessing a hybrid network.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objective of the Flexnet project is to build a new paradigm for flexible network communication and thus promote IoT value creation. The Flexible IoT network provides IoT value creators the opportunity to consume network communication on demand in real time, automatically and according to specific needs. On a European level the Flexnet Celtic-plus consortium consists of 14 partners from 5 different countries. The Belgian consortium wishes to focus on maintaining the mission-critical communication during emergency situations, by supporting a flexible and reliable communication network. The connectivity between different heterogeneous wireless networks will be orchestrated by a new platform (imec) that integrates all these technologies in a single, cirtual and highly configurable network infrastructure. Micro-services will be rolled out autonomously and migrated live, based on monitored IoT context and application requirements.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The objective of the European SARWS Celtic-plus consortium (over 30 partners from 7 countries) is to provide real-time services that ensure scalable, robust, secure, efficient, safe and energetically sustainable smart mobility. To improve road safety, the Flemish partners will research the use of crowd-sourced vehicle data to enable real-time warning services for local weather phenomena and dangerous road conditions that surpass the accuracy and timeliness of current warning systems. Local weather data will be gathered from the CAN/OBD-bus and external sensors using In-Car Smart Sensor Nodes (ICSSN), (VPS, IMEC) using a secure data distribution framework (Inuits). Initial data is obtained from IMEC and VPS test vehicles. Once prototypes are completed, 30 bpost vehicles will be equipped with ICSSNs. Local weather conditions will be extracted from the collected data using distributed machine learning algorithms (IMEC, Be-Mobile, VPS, RMI) for application in the following use cases: (i) time-series data analytics for weather-related vehicle behaviour, (ii) validating and improving the accuracy of weather and road weather models, (iii) real-time weather services that warn drivers and other stakeholders (e.g. AWV) about dangerous road conditions and an In Car Driver App (ICDA) will allow the driver to interact with the system (notifications, event tagging). Concrete objectives and criteria Primary targeted weather conditions are visibility (e.g. fog) and road condition (slipperiness, aquaplaning, snow, black ice). Secondary targets are precipitation (intensity, type), local temperature and wind gusts (crosswind in particular) and will be considered if research on the primary targets is successfully completed. Smart sensing: Define a methodology for selection, calibration and fusion of sensors, CAN signals and user feedback for each of the primary (and by extension secondary) targets. Data Distribution: Design a scalable hardware and software platform that allows data collection from a large vehicle fleet (30 bpost vehicles in SARWS, potentially the full fleet of 6500 after the project) - for multiple weather conditions (see primary and secondary sensing objectives) - using limited bandwidth (3G, 144kbit to 2Mbit depending on vehicle speed) to transmit vehicle data (up to 25GB/h per vehicle) in real-time without significant information loss, through data compression, reduction, collection and code distribution methodologies. - using limited in-vehicle hardware resources (i.e. a smartphone-grade embedded CPU in the ICSSN) - that is automatically optimized for specific data collection tasks depending on the required information using adaptive code distribution. - that is expandable with future applications by defining software interfaces and a methodology on KPI analysis and code distribution so that future software components can be made compatible. Data Processing: - Define a methodology for classifying weather conditions from mixed data streams (CAN, sensors and user feedback) given driver actions, vehicle behavior and low-resolution regional weather data. - Verification and real-time adjustment of NWP output using this new source of highly localized data - Extend the state-of-the-art in road weather models by blending classical inputs (NWP , radar, road weather stations) with crowd-sourced car sensor data. - Demonstrate real-time road weather warning services for 250m road sections, for road managers such as AWV and drivers based on this new generation of road weather models. Privacy: Identify and research technical and organizational privacy measures to (1) comply with GDPR and (2) that allow large-scale, real-time data collection without loss of road weather information, validated using KPI-analysis and regression testing. Security: Define a secure architecture, including end-to-end encrypted V2I/I2V and soft/hardware measures ensuring read-only CAN-bus access to prevent the ICSSN from becoming an intrusion point for the vehicle

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Antwerp is the economic stronghold of Flanders, thanks to its world port, the second largest chemical cluster, a strong creative sector, but above all a joint ambition to grow the region through innovation. The university's role has broadened to become a driving force for innovation, an innovative regional force in knowledge-intensive ecosystems. On the one hand by delivering well-trained people; on the other hand, by responding to the specific needs of the innovation ecosystem in which the university is embedded. Based on the strengths of the University of Antwerp combined with the characteristics of the Antwerp ecosystem, the university focuses on three valorisation domains. A pre-incubation structure is being set up within each of these domains to support and strengthen the valorisation processes within these different domains. This is an open innovation hub where physically the different actors of the relevant innovation ecosystem can meet to work together on innovation projects and to follow training programs. One of these valorisation domains set up is metropolitan areas, smart city, mobility and logistics. In this domain, collaborations on IoT and AI projects with applications in smart city, smart industry, smart port and logistics, smart mobility and smart building are set up from The Beacon. The Beacon is the result of a unique collaboration between the city, the port, the university, IMEC, Lantis and Agoria.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Bracke Ilse

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

PortForward is applying a holistic and modular approach for the development of a port operations management platform for small and medium sized ports. The expected outcome will lead to a smarter, greener and more sustainable port ecosystem. For PortForward, the Port of the Future will be: i. Smart, using state of the art Information and Communication Technologies (ICT) solutions, such as Internet of Things (IoT) for port assets, smart sensors and networks, the Virtual Port concept, Augmented Reality for port operations; ii. Interconnected, with other transportation modes, e.g. road transport, railway, focusing on short sea shipping and inland waterways; iii. Green, through the adoption of green technologies, through the novel Green Yard Scheduler and Life Cycle Analysis for sustainable port operations. PortForward envisions a 10% reduction in port emissions, combined with a 10% reduction of its total operational costs. 5 use cases have been selected in Italy, Spain and Germany. An Advisory Board will be formed by field experts, who will provide feedback and guidance to the consortium.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Although the foundations of Artificial Intelligence (AI) have been around for a long time, advances in computational performance and research in novel AI techniques have led to a revival of this research domain. With the advent of the Internet of Things (IoT), numerous "smart" applications driven by AI, have found their way into our everyday lives. Due to the computational complexity of these techniques, currently a common approach is to minimize the computations performed on the user's device and to perform the bulk of the work in a cloud environment. However, with a foresight of over 20 billion smart devices by 2020, handling this data with a cloud-centric approach cannot be maintained. In order to continue the AI revolution, alternative approaches are needed in which the AI is distributed across devices closer to the edge of the IoT network. Current AI solutions mostly focus on large-scale cloud environments or high performance devices. IoT devices, however, are very diverse in hardware architecture and often constrained in resources. Depending on the hardware and software constraints (e.g. timing requirements, computational, memory and energy constraints), tailored optimization strategies are needed. In order to allow distribution of AI algorithms in such a diverse environment, two gaps in the state of the art need to be bridged. In this research project, we will investigate (1) a systematic analysis method for Artificial Intelligence to determine the characteristics of these algorithms and (2) define a method for optimal distribution of AI in the context of IoT.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Mercelis Siegfried

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project wants to Perform a first initial study to scope the work and derisk the project to investigate whether Al can help in automating the sampling, design a neural network based approach to automatically classify the electronic devices category and validate this on the existing Recupel labelled dataset; heikkust

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project is part of the IMEC Frame Agreement and is being given as structural investment for fundamental research based on yearly set KPIs from the group to IMEC. This A-budget is defined within the IMEC Way of Working and part of the frame agreement of the University of Antwerp and IMEC.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Research on performance optimization of the physical layer of communication networks has been focused on the development of transmission techniques such as MIMO and OFDM/DMT that effectively exploit the space and frequency dimension. However, the physical layer is usually configured statically and thus fails to properly exploit the time dimension. Upper layers in the protocol stack hold crucial information on the time dependent nature of the network traffic, which can indeed be exploited by the physical layer to dynamically select different configurations and increase overall network efficiency. Therefore, the aim of this project is to develop real-time adaptive physical layer control algrithms that can be combined with existing upper layer network functions so as to additionally exploit the time dimension optimally. The possibilities and performance gains of real-time adaptive physical layer control will be explored in the context of fifth generation broadband copper access networks (5GBB). 5GBB envisages a hybrid fiber-DSL deployment where the fiber network is terminated near the boundary between public and private property. In such deployment, the reduced copper loop length, together with the use of specific (e.g. full duplex) transmission techniques, enables data rates of up to 10 Gbps. In addition, the specificity of the deployment scenario is to make, for the first time, the implementation of real-time adaptive physical layer control algorithms a feasible objective.

Researcher(s)

• Promoter: Blondia Chris
• Co-promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Existing wireless technologies often exhibit poor performance in very dense networks, where hundreds or even thousands of stations need to connect to the same access point. This is mostly caused by the increased probability of two devices transmitting data at the same time, which causes the data packets to collide and be lost. Recently, station grouping has been proposed as a new method for collision-free data transmission in these dense environments. The basic idea is that stations are split into groups and each group is given a specific time interval during which only its members can transmit. This limits the maximum simultaneous transmissions, and therefore potential collisions. A station grouping configuration has many degrees of freedom: the number of groups, their duration, and which stations belong to each group. Several algorithms have been proposed to determine the optimal configuration as a function of the number of stations and their traffic demand. However, they all have several shortcomings that we aim to address in this project: they assume very specific and static traffic patterns, they do not optimise the trade-off between energy consumption and performance, and they cannot avoid interference among multiple overlapping networks. In this project, we will develop novel accurate mathematical models, based on Markov chains and supervised machine learning, that accurate estimate the energy consumption and throughput performance of specific station grouping configurations. This will be used to develop real-time station grouping algorithms that can handle heterogeneous stations and traffic, and adapt to changes in the traffic patterns. Finally, the algorithm will be extended to multiple access points, and used to implement interference avoidance mechanisms. The resulting solution will be evaluated using simulation to assess scalability, and will be implemented on real hardware to assess real-time execution time constraints.

Researcher(s)

• Promoter: Famaey Jeroen
• Fellow: Santi Serena

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project is part of the IMEC Frame Agreement and is being given as structural investment for fundamental research based on yearly set KPIs from the group to IMEC. This A-budget is defined within the IMEC Way of Working and part of the frame agreement of the University of Antwerp and IMEC.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project is part of the IMEC Frame Agreement and is being given as structural investment for fundamental research based on yearly set KPIs from the group to IMEC. This A-budget is defined within the IMEC Way of Working and part of the frame agreement of the University of Antwerp and IMEC.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project is part of the IMEC Frame Agreement and is being given as structural investment for fundamental research based on yearly set KPIs from the group to IMEC. This A-budget is defined within the IMEC Way of Working and part of the frame agreement of the University of Antwerp and IMEC.

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project combines the USP of different imec groups on localisation and focusses on massive localisation systems (long range, large density) using sub-Ghz, but also includes large density accurate localisation using UWB.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The greenhouse horticulture sector in the border region Flanders - the Netherlands has a very high productivity, is innovative and takes second place as the exporting region in the world. Smart crossovers between greenhouse horticulture with high-tech systems and materials can ensure a strengthened and future-oriented position. The specific cross-over between sensor technology and horticulture offers great opportunities but is still insufficiently used. GROW! changes that.

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Sluydts Vincent

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

General objective: The main goal of this project is to develop a design approach and the necessary computational tools that enable the concurrent design of application software, embedded software and hardware platforms, ensuring the targeted closed-loop performance of cyber physical systems. This with the aim to increase the efficiency of the design process and yet reducethe costs of the associated embedded software and hardware platforms. Concrete goals: More specifically, the innovation goals of this project are to: 1. Develop a methodology and software tools to support the concurrent design of application software and embedded platform for individual cyber-physical product variants: - enabling both control engineers and embedded platform engineers to perform a trade-off analysis between various design choices on application and platform level in an agile manner, i.e. without long iteration loops, thereby reducing the typical development time of an embedded control application with at least 25%. - improving the cost-effectiveness of embedded platforms by at least 10%, by considering stochastic delays instead of using 'worst case' response times and bus delays, without sacrificing the stability, performance and robustness of the closed-loop behaviour. 2. Investigate the feasibility of extending the above approach with design space exploration techniques that automatically select the most optimal design alternative in terms of application/platform design choices in the large space of possible solution alternatives. 3. Develop an approach and software tools to support trade-off analysis and design space exploration for the embedded platform selection and design in the case of complete mechatronic/cyber-physical controller product lines. Building further on these methods and tools, the company partners in this project aim to realize the following targets: Atlas Copco's main goal is to create an approach, a software framework and the accompanying development tools that support their designers responsible for implementing the compressor room control to select the most appropriate software and hardware platform deployment and configuration, guaranteeing the required compressor room performance under all circumstances. Picanol wants to increase the performance and quality of its weaving machines by improving the co-design between the control software and embedded platform engineers. More specifically, Picanol wants to deploy this co-design approach to the yarn insertion subsystem of all machine variants, thereby increasing the production capacity of these variants with 2% or reducing the air consumption with the same amount. Tenneco's main goal is to select a set of embedded and power electronics hardware platforms that cost-optimally cover their complete product line of electro-magnetic shock absorbers from low-end to high-end vehicles. The approach and tools that allows to select this set of platforms should also be applicable to other Tenneco product lines. Michel Van de Wiele (MVDW) wants to select a new, durable and modular embedded hardware and software platformthat is capable of controlling today's and tomorrow's weaving machinery. Specifically, for the same loom requirements a reduction of the hardware cost by at least 10 % is targeted or with the same hardware cost, the target is to realize an increase in machine speed of 10 to 50 % or being able to deal with at least 10 % more sensors / actuators. Next to this, MVDW also aims to update their design approach and tools such that designers can easily predict a priori if the embedded controller for a particular variant

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: De Meulenaere Paul

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Within the Smart Highway project, MOW and imec will build a high tech test environment to support automated driving along (a part of) a highway [10-20 km E313 and a part of the ring of Antwerp R01] combined with a regional road [Turnhoutsebaan N12 towards the city centre of Antwerp]. These roads will be equipped with wireless communication (both 802.11p and LTE-V) and sensor technologies, and concrete test cases will be set up to test these technologies for supporting connected and automated vehicles, based on real-life monitoring and analysis. Besides imec and MOW, also KU Leuven and Flanders Make will contribute to the project.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Within the Smart Highway project, MOW and imec will build a high tech test environment to support automated driving along (a part of) a highway [10-20 km E313 and a part of the ring of Antwerp R01] combined with a regional road [Turnhoutsebaan N12 towards the city centre of Antwerp]. These roads will be equipped with wireless communication (both 802.11p and LTE-V) and sensor technologies, and concrete test cases will be set up to test these technologies for supporting connected and automated vehicles, based on real-life monitoring and analysis. Besides imec and MOW, also KU Leuven and Flanders Make will contribute to the project.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Within the Smart Highway project, MOW and imec will build a high tech test environment to support automated driving along (a part of) a highway [10-20 km E313 and a part of the ring of Antwerp R01] combined with a regional road [Turnhoutsebaan N12 towards the city centre of Antwerp]. These roads will be equipped with wireless communication (both 802.11p and LTE-V) and sensor technologies, and concrete test cases will be set up to test these technologies for supporting connected and automated vehicles, based on real-life monitoring and analysis. Besides imec and MOW, also KU Leuven and Flanders Make will contribute to the project.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the City of Things initiative, imec, the city of Antwerp and the Flemish Region are working together to make Antwerp a large-scale testbed for the testing and development of smart city technology. With this unique project we want to become a driving force for research into - and the development of - smart cities.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the City of Things initiative, imec, the city of Antwerp and the Flemish Region are working together to make Antwerp a large-scale testbed for the testing and development of smart city technology. With this unique project we want to become a driving force for research into - and the development of - smart cities.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Cognitive Wireless project aims at developing an AI layer which is able to quickly detect both the technology of other nodes in the network as the type of traffic purely based on IQ samples. For this, a deep learning framework will be developed which is able to classify from the pure IQ samples to a series of pre-determined technologies (WiFi, Bluetooth, etc.) and traffic classes (bursty traffic, narrowband traffic, etc.).

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Better Than Wired project aims at providing deterministic access to wireless networking so that the same level of Quality of Service guarantees can be given to wireless networks as wired networks feature, with an additional benefit of an increased flexibility. The project will mainly evaluate an industry 4.0 scenario where a plant wants to optimise their connectivity. By combining existing building blocks on programmable network management (e,.g., ORCHESTRA, real-time SDR),the project will feature a high level of flexibility in managing the wireless network. As such, it is possible to quickly anticipate to changes in performance.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project combines the USP of different imec groups on localisation and focusses on massive localisation systems (long range, large density) using sub-Ghz, but also includes large density accurate localisation using UWB.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project is part of the IMEC Frame Agreement and is being given as structural investment for fundamental research based on yearly set KPIs from the group to IMEC. This A-budget is defined within the IMEC Way of Working and part of the frame agreement of the University of Antwerp and IMEC.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

OCTA is hardware/software fast prototyping platform for Internet-of-Things applications. In this project the platform is further designed and developed to support interdisciplinary research with Internet-of-Things.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project combines the USP of different imec groups on localisation and focusses on massive localisation systems (long range, large density) using sub-Ghz, but also includes large density accurate localisation using UWB.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The RAMSES consortium is working toward commercialising the EmergencyEye, a system designed to allow ambulance dispatchers to have better communication with patients and bystanders in the field. The innovation, which supports emergency dispatchers with speedy geolocation, diagnosis and audio-visual guidance of resuscitation measures, was tested in a pilot in Rhein-Kreis-Neuss, Germany.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In this project we develop and validate an IoT pre-commercial product in the area of industry 4.0 for the (petro)chemical sector. The project focusses on algorithm optimization, power consumption optimization, communication energy budget classification, validation and demonstration in an operational industrial environment and potential patent application.

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Berkvens Rafael

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Existing wireless technologies often exhibit poor performance in very dense networks, where hundreds or even thousands of stations need to connect to the same access point. This is mostly caused by the increased probability of two devices transmitting data at the same time, which causes the data packets to collide and be lost. Recently, station grouping has been proposed as a new method for collision-free data transmission in these dense environments. The basic idea is that stations are split into groups and each group is given a specific time interval during which only its members can transmit. This limits the maximum simultaneous transmissions, and therefore potential collisions. A station grouping configuration has many degrees of freedom: the number of groups, their duration, and which stations belong to each group. Several algorithms have been proposed to determine the optimal configuration as a function of the number of stations and their traffic demand. However, they all have several shortcomings that we aim to address in this project: they assume very specific types of traffic, they cannot be executed in real-time, they cannot handle changes in traffic demand, they cannot provide Quality of Service differentiation among stations, and they cannot optimise multiple overlapping networks. The resulting solution will be evaluated using simulation to assess scalability, and will be implemented on real hardware to assess execution time constraints.

Researcher(s)

• Promoter: Famaey Jeroen
• Fellow: Akbar Raja Usman
• Fellow: Sultania Ashish Kumar

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

CONCORDA contributes to the preparation of the European motorways for automated driving and high density truck platooning with adequate connected services and technologies. The main objective of the Action is to assess performances (reliability/availability) of hybrid communication systems, combining 802.11p and LTE connectivity, under real traffic situations. The study prepares also the improvement of the localisation services. As part of the project a validation and demonstration of the developed methods for self-organisation is carried out through extensive simulation experiments, assessing the achievable cost reductions and performance enhancements.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

CONCORDA contributes to the preparation of the European motorways for automated driving and high density truck platooning with adequate connected services and technologies. The main objective of the Action is to assess performances (reliability/availability) of hybrid communication systems, combining 802.11p and LTE connectivity, under real traffic situations. The study prepares also the improvement of the localisation services. As part of the project a validation and demonstration of the developed methods for self-organisation is carried out through extensive simulation experiments, assessing the achievable cost reductions and performance enhancements.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

FUTEBOL will create research infrastructure and tools that enable and promote the federation of experimental telecommunication resources irrespective of their location in Brazil and Europe, with a view toward global experimentation across heterogeneous networks, both wired and wireless, and a variety of end-systems. The focus of FUTEBOL is on building upon current tools and platforms in support of end-to-end experimentation, creating a pool of, and giving open access to, shared network experimental resources that complement those available in each continent. Industrial and academic researchers in telecommunications have recognized the need for flexibility, intelligence, and the efficient use of resources. These requirements are exemplified by the broad range of technologies being proposed for inclusion into the fifth generation of cellular networks (5G). Furthermore, existing overarching strategies stress the intelligent combination of techniques to flexibly use available resources, both wireless and wired, in the most efficient way. In this sense, FUTEBOL is an experimentation platform that will support the joint optimization of optical network scheduling and radio resource management. Such a platform is required for experimentation-based exploration and validation of several 5G technologies (e.g. cloud radio access networks, cell densification, etc.) and the efficient use of the optical backhaul. The development of a coherent toolset to support joint control of wireless and optical networks in an experimentation context is among the objectives of FUTEBOL, including: (1) to create a toolset to enable experimentation at the wireless/optical network boundary; (2) to provide these tools to an array of experimental facilities that support experiments in both wireless and optical networks in an open manner; and (3) to contribute to open research questions in optical/wireless using the tools developed in the project. The basic experimentation toolset created in FUTEBOL will consist of a defined environment that allows users to focus directly on their problems of interest with minimal overhead. As such, a common experimentation architecture, relevant to issues that cross the wireless/optical network boundary, is the core of this toolset. The architecture will enable the coordination between wireless and optical networks, defining interfaces to provide a standard method of communicating control and management data between each type of network. The toolset based on these elements will support experimentation by providing both a realistic model of real deployments of wireless/optical integration points as well as additional experiment monitoring capabilities that may not be available in real deployments. Furthermore, the toolset must be portable across testbeds to provide a consistent framework independent of the particulars of a given facility. FUTEBOL will also provide open access to the developed tools, through methods such as those defined by the Fed4FIRE project.

Researcher(s)

• Promoter: Marquez-Barja Johann

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

The goal of this project is to develop a robust system for collecting and managing reliable, dynamic geospatial information on road infrastructure and environment, that replaces high quality but occasional monitoring with opportunistic continuous massive data collection and analysis.

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of MAGICIaN is to overcome the limitations of out-of-the-box NB-IoT that prevent it from being used for mission-critical applications. Specifically, the standard does not natively support QoS differentiation or seamless handover mechanisms. We will design and develop an end-to-end network management solution that brings QoS guarantees on top of out-of-the-box best-effort NB-IoT networks. The MAGICIaN solution consists of two parts. The network controller interfaces and interacts with the NB-IoT access network (i.e., eNodeBs) and the different components in the Evolved Packet Core (EPC) to bring valueadded services on top of the best effort network.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Markov processes have found widespread use in the analysis of computer systems and beyond. Over time the size of the systems under consideration has grown considerably, e.g., Google has hundreds of thousands of servers located in its various data centers. This growth in the system size has made conventional methods to analyse these Markov processes infeasible. As such, deterministic approximations, also known as mean field or fluid models, have been introduced to analyse such large scale systems. Interestingly, these deterministic models have been shown to correspond to the limit of a sequence of appropriately scaled Markov processes showing that the systems behaviour becomes deterministic as the system size tends to infinity. These Markov processes typically have a countable state space and the limiting system is described by a set of ordinary differential equations. However, in order to analyse large scale computer systems with general job size distributions, one needs to keep track of the age or residual service time of each job. This makes the state space uncountable and the natural candidate for the limiting system becomes a set of partial differential equations (PDEs). The aim of this project is to develop PDE mean field models for large scale computer systems, to establish convergence results and to use these models to gain insight into the system behaviour. The project combines techniques from stochastic modelling, probability, numerical analysis and simulation.

Researcher(s)

• Promoter: Van Houdt Benny
• Fellow: Hellemans Tim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of the FUSE imec.icon project is to do research on and prototype the technical enablers for a flexible federatable service platform allowing to develop, run and manage unified micro-services.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Observer aims to make the traffic lights in the city so smart that they can also "think around" unforeseen problems. We gather information about moving and stationary vehicles using cameras and traffic counters, about how many vulnerable road users there are, about any priority vehicles on the road, etc. We then combine this data with known statistics about normal and abnormal traffic volumes so that we can create a clear picture of how the traffic should be when all of these variables are taken into account. Should some traffic lights stay longer on green? Or shorter? Should the speed limit be adjusted? Day and night, without anyone having to keep an eye on things.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Observer aims to make the traffic lights in the city so smart that they can also "think around" unforeseen problems. We gather information about moving and stationary vehicles using cameras and traffic counters, about how many vulnerable road users there are, about any priority vehicles on the road, etc. We then combine this data with known statistics about normal and abnormal traffic volumes so that we can create a clear picture of how the traffic should be when all of these variables are taken into account. Should some traffic lights stay longer on green? Or shorter? Should the speed limit be adjusted? Day and night, without anyone having to keep an eye on things.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

How much fine dust and electromagnetic radiation is there in the air? The most fundamental ingredient for building a smart city is having access to accurate, relevant and real-time data. What is the air quality in this or that street? How much nitrogen and ozone is there in the air at that intersection? Is the concentration of fine dust the same all over this district? And what's the average particle size? The more we measure, the more we can calculate, forecast and use the data in handy applications: an app that calculates healthy cycle routes, a tool that moves traffic in the right direction, fact-based changes that can be used when a road is being rebuilt, etc. The Dencity project aims to show how big the impact will be on the relevant data we have available – and whether we need to add even more sensors to the city than there are today. We're also looking to see if all of these sensors truly have to be top-quality and which cheaper instruments are available for supplying data of sufficiently high quality.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Flooding project combines real-time information from sensors in drains and streams, using information from radar images and other useful weather-related input. This information can then be translated into usable information. For instance, the fire brigade knows where best to deploy its manpower, what precautions need to be taken and whether neighboring brigades and resources will have to be brought in. Residents can also be properly informed, with warnings telling them exactly when the risk times will be and what action they need to take. The managers of the drainage system can also be quicker in locating where problems are likely to occur in the network, which goes hand in hand with proactive maintenance, etc.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the SPIDER ESA project of Antwerp Space, Imec gives technological advice on data link and network layer protocol design for small satellite constellations and implementation and simulation in the ns-3 event-based network simulator.

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The IoT domain is characterized by many applications that require low-bandwidth communications over a long range, at a low cost and at low power. This has given rise to novel 'SIM-less' radio technologies that try to fill in this existing market gap of low-power wide area IoT networks, often referred to as Low Power Wide Area Networks or LPWANs. Due to the use of sub-GHz radio frequencies (typically 433 or 868 MHz), a single LPWAN base station has a large coverage area, with typical transmission ranges in the order of 1 up to 50 kilometres. As a result, a single base station can support high numbers of connected devices (> 1000 per base station), allowing a broad range of new technology companies to easily enter the IoT market. Currently, several sub-GHz technologies are being promoted simultaneously, all of which use the same (limited) wireless spectrum. Notorious initiatives in this domain are LoRa, SigFox, IEEE802.15.4g and the upcoming IEEE 802.11ah (or "HaloW") standard. However, many of these technologies are still in their infancy, and optimizations regarding a.o. quality of service, roaming, and service management are still lacking. In addition, since the amount of available spectrum is much smaller and the propagation ranges much larger, these technologies will cause interference at much larger scale, leading to severe inter-technology and inter-operator interference. If left unchecked the unlicensed sub-1GHz bands will soon be congested and unreliable. To avoid this fate, the IDEAL-IoT project will design and develop advanced, highly configurable networking components, combined with a coordination framework to uniformly manage and optimize an ecosystem of coexisting wireless sub-GHz LPWANs. More specifically, the project will investigate and develop novel & scalable networking solutions at three levels. 1. At intra-technology level, IDEAL-IoT will improve the performance of existing LPWAN networks. This objective includes (i) increasing the scalability of existing networks through the design and optimization of advanced PHY and MAC protocols for LPWAN networks; (ii) designing intelligent solutions to support real-time LPWAN traffic with latencies below 100 msec and reliability higher than 99.99%; (iii) improving energy efficiency by a factor 2 through PHY&MAC co-design. 2. At inter-technology level, IDEAL-IoT will improve the performance of coexisting LPWAN networks from different operators as well as provide coexistence between different LPWAN technologies. This objective includes: (i) reducing packet loss due to interference by 50% through interference detection, interference mitigation strategies and inter-technology LPWAN communication, negotiation and optimization; (ii) providing inter-technology roaming and multi-hop communication. 3. At management level, IDEAL-IoT will realize technology agnostic solutions for virtualized LPWAN network management and intelligence. This objective includes: (i) technology-agnostic virtualized components and light-weight APIs for real-time creation of virtualized LPWANs, on-the-fly adjustment of SLAs and dynamic installation of virtualized functionalities to control and improve interactions over LPWAN networks; (ii) the design of a cloud repository capable of optimizing LPWAN settings 10 times faster than is currently the case; (iii) realizing fully reliable over-the-air, reconfigurations and partial software updates of large groups of devices with 50% lower latency and 80% less network overhead.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of MuSCLe-IoT is to design the necessary algorithms and protocols for both IoT devices and backend systems to support such multimodal communication and localization. Key innovations are planned in terms of inter-technology load balancing and routing, as well as multimodal GPS-less accurate indoor and outdoor localization. Particular attention will be paid to restrict the resulting footprint, signaling overhead and application impact to a bare minimum. A prototype solution will demonstrate the combined use of Sigfox, LoRa, DASH7 and 802.15.4g

Researcher(s)

• Promoter: Weyn Maarten
• Co-promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

The goal of the 5GUARDS project is to investigate, evaluate and demonstrate how various services with diverse requirements can be simultaneously supported by the future 5G network based on the concept of network slicing. 5GUARDS envisions that three building blocks will contribute to the realization of services with diverse requirements: core slicing, RAN slicing and dynamic software reconfiguration.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Experimentally driven research is considered to be a key factor for growing the European Internet industry. In order to enable this type of RTD activities, a number of projects for building a European facility for Future Internet Research and Experimentation (FIRE) have been launched, each project targeting a specific community within the Future Internet ecosystem. Through the federation of these infrastructures, innovative experiments become possible that break the boundaries of these domains. Besides, infrastructure developers can utilize common tools of the federation, allowing them to focus on their core testbed activities.Recent projects have already successfully demonstrated the advantages of federation within a community. The Fed4FIRE+ project intends to implement the next step in these activities by successfully federating across the community borders and offering openness for future extensions.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Internet of Things is fostering more and more mission critical applications on top of the wireless infrastructure. An example of this is the control of drones, which requires ultra-reliable communication with ultra low latency guarantees and the ability to switch from one technology to the other. Current IoT networks are currently not suited for providing such guarantees as (i) each technology works independently of each other, (ii) applications sometimes have limited control over the devices that are part of the network and (iii) existing high performing management solutions (e.g., Software Defined Networking) only work with resource rich devices. In this project, I propose a way to reach the same level of flexibility in the management of IoT networks as these high performing management solutions offer, without losing the support for resource constrained nodes and third party devices. We do this through WHISPER, an approach that generates "small lies" (fabricated messages) about the network state with the goal of improving the overall network management and providing guarantees on the application delivery. These messages are used to fool the existing IoT MAC, network and transport protocols in such a way that WHISPER will take control over the full network. This includes routing, link and end-to-end communication. As such, WHISPER can be used to manage a multi-technology IoT environment, where mission-critical applications such as drones can be hosted.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Famaey Jeroen
• Fellow: Mennes Ruben

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The High Impact project of IMEC aims at stimulating fundamental research that can benefit in the long term the valorization of the group. Within this project, the following research lines have been funded: - Appdaptive: configuring IoT networks based on application requirements - Participation in the DARPA Spectrum Collaboration challenge - Densenets: SDN-based network management of wireless networks (resulting in the ORCHESTRA framework) - SubWAN: management of new low power wide area wireless networks

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Cities are relying on Internet of Things (IoT) to make their infrastructure smart by using advanced sensing and control devices within the city's infrastructure with the goal of improving urban living, city's experience, etc. Analysis of the data generated by a wide range of sensors and actuators allows controlling the city in a better and more automated way, with respect to e.g. the view on the city's mobility patterns. To realize a smart city infrastructure we consider three layers: the network/sensor layer, i.e. a city-wide network based on a variety of communication technologies and its protocol stacks together with a variety of sensors allowing the collection of raw data; a data layer, dealing with the continuous stream of data and its techniques for processing, storing, mining; an application layer, responsible for interpreting the processed data stream for more optimally controlling the city. The network/sensor layer will be covered by the MOSAIC research group (Dept. Mathematics and Computer Science, Chris Blondia and Steven Latré), while the data layer will be dealt with by the ADREM research group (Dept. Mathematics and Computer Science, Bart Goethals) and finally the application layer is the responsibility of the Transport and Regional Economics research group (Dept. Transport and Regional Economics, Eddy Van de Voorde and Thierry Vaneslander). The general aim of this project is to bring together the expertise present at the University of Antwerp at each of these layers, in order to bundle the research and come up – through an intensive collaboration - with a framework covering the three layers. More specifically, we will build an integrated smart city platform, tailored towards mobility, that allows to capture, process, analyze, interpret and control smart city data in general and mobility data particularly. As discussed in the next section, this will result in important novel research contributions in each of the three layers and will result in a proof-of-concept where the research results are combined into a demonstrator.

Researcher(s)

• Promoter: Blondia Chris
• Co-promoter: Goethals Bart
• Co-promoter: Latré Steven
• Co-promoter: Van de Voorde Eddy
• Co-promoter: Vanelslander Thierry

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

SynchroniCity represents the first attempt to deliver a Single Digital City Market for Europe by piloting its foundations at scale in 11 reference zones - 8 European cities & 3 more worldwide cities - connecting 35 partners from 11 countries over 4 continents. Building upon a mature European knowledge base derived from initiatives such as OASC, FIWARE, FIRE, EIPSCC, and including partners with leading roles in standardization bodies, e.g. ITU, ETSI, IEEE, OMA, IETF, SynchroniCity will deliver a harmonized ecosystem for IoT-enabled smart city solutions where IoT device manufacturers, system integrators and solution providers can innovate and openly compete. With an already emerging foundation, SynchroniCity will establish a reference architecture for the envisioned IoT-enabled city market place with identified interoperability points and interfaces and data models for different verticals. This will include tools for co-creation & integration of legacy platforms & IoT devices for urban services and enablers for data discovery, access and licensing lowering the barriers for participation on the market. SynchroniCity will pilot these foundations in the reference zones together with a set of citizen-centred services in three highimpact areas, showing the value to cities, businesses and citizens involved, linked directly to the global market. With a running start, SynchroniCity will serve as lighthouse initiative to inspire others to join the established ecosystem and contribute to the emerging market place. SynchroniCity takes an inclusive approach to grow the ecosystem by inviting businesses and cities to join through an open call, allowing them to participate on the pioneering market place enabling a second wave of successful pilots. They will strengthen the ecosystem by creating a positive ripple effect throughout Europe, and globally, to establish a momentum and critical mass for a strong European presence in a global digital single market of IoT-enabled solutions.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The High Impact project of IMEC aims at stimulating fundamental research that can benefit in the long term the valorization of the group. Within this project, the following research lines have been funded: - Appdaptive: configuring IoT networks based on application requirements - Participation in the DARPA Spectrum Collaboration challenge - Densenets: SDN-based network management of wireless networks (resulting in the ORCHESTRA framework) - SubWAN: management of new low power wide area wireless networks

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The High Impact project of IMEC aims at stimulating fundamental research that can benefit in the long term the valorization of the group. Within this project, the following research lines have been funded: - Appdaptive: configuring IoT networks based on application requirements - Participation in the DARPA Spectrum Collaboration challenge - Densenets: SDN-based network management of wireless networks (resulting in the ORCHESTRA framework) - SubWAN: management of new low power wide area wireless networks

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Project with imec funding on the High Impact topic of wireless communication, specific focus on LPWAN (DASH7, Lora, LoraWAN and Sigfox) and localization (mostly signal strength based). Moreover cooperation with other groups by using OCTA fast prototyping tools.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The High Impact project of IMEC aims at stimulating fundamental research that can benefit in the long term the valorization of the group. Within this project, the following research lines have been funded: - Appdaptive: configuring IoT networks based on application requirements - Participation in the DARPA Spectrum Collaboration challenge - Densenets: SDN-based network management of wireless networks (resulting in the ORCHESTRA framework) - SubWAN: management of new low power wide area wireless networks

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the City of Things initiative, imec, the city of Antwerp and the Flemish Region are working together to make Antwerp a large-scale testbed for the testing and development of smart city technology. With this unique project we want to become a driving force for research into - and the development of - smart cities.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In the City of Things initiative, imec, the city of Antwerp and the Flemish Region are working together to make Antwerp a large-scale testbed for the testing and development of smart city technology. With this unique project we want to become a driving force for research into - and the development of - smart cities.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Proof of Concept Cofunding for Aloxy, which has the ambition to improve safety and efficiency, to automate processes eand to deliver actionable insights into industrial operations by means of a modular Internet of things platform targeting the petrochemical industry.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Service assignment for Flemish Institute for Logistics in function of the Win4Track project: inventory of LPWAN systems and matching on the applications and criteria developed by the partners in this project.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project defines the right criteria for an efficient IoT architecture for city logistics. In addition, the added value of the use of this IoT architecture is demonstrated on the basis of specific logistic applications in relation to city logistics.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The immediate cause of the SeRGIo project is the realization by key industrial players that their mobile workforce (e.g. BPost) and customers (e.g. Mobile Vikings/CityLife) are a significant untapped resource that can be used to support new mobile sensing applications. SeRGIo will tap into the huge potential of qualitative mobile sensing applications, and address the complexity (and cost) to develop and deploy mobile sensing applications with state-of-the-art software platforms. The SeRGIo project focuses on qualitative and quantitative mobile sensing at urban scale. This proposed approach complements conventional Smart City platforms with a novel "human as a sensor" paradigm that leverages mobile workforces, groups of incentivised citizens and their mobile devices, to provide sensed data at a far greater scale accuracy and resolution than was previously possible. The SeRGIo consortium argues that to truly unleash the potential of Smart Cities and acquire a complete view on urban dynamics, it is equally important to capture subjective, qualitative metrics in addition to the quantified metrics that are typically collected via physical sensors. Examples of qualitative metrics include perceptions of neighbourhood safety, amiability, cleanliness, subjective experience with urban services, shopping experiences, etc.. The project is motivated by three high-potential business cases (from Nokia, bpost and CityLife), which demand high quality sensed data and professional data analytics. Although the business potential of mobile sensing is clear, it is far from trivial to design and develop a solution that can live up to the expectations of the above business cases. An acceptable solution (1) must collect qualitative as well as quantitative data and exploit synergies by mixing both sources of data, (2) precisely target the allocation of sensing tasks to users based upon their spatio-temporal context, and (3) maximise performance and minimise energy consumption for mobile devices, while ensuring security and privacy. Today's off-the-shelf mobile sensing solutions fail to deliver these requirements. SeRGIo will address them by investigating: 1. A family of domain-specific languages to formalise the business requirements of sensed data customers and inform the orchestration service on how to select the most appropriate subset of mobile users from the participant population. 2. An orchestration service automates the distribution of sensing tasks to a targeted selection of potential end-users based on their location, activities, situational context and device capabilities learned from multi-model behaviour analytics. 3. A flexible and modular data acquisition architecture enables seamless interoperability between platform-independent HTML5 sensing modulesand platform-optimized native accelerators that are downloaded and configured dynamically based upon application demands. This architecture will be populated with a suite of accelerators that use platform-specific features (e.g. Digital Signal Processors, Graphical Processing Units) to achieve orders-of-magnitude improvements in performance and energy efficiency. 4. A modular, lightweight, and multi-model data analytics framework that allows for the on-device assembly of custom data analytics pipelines that combine high-quality quantitative sensor data with insightful qualitative data.

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project funds the IDLab valorisation manager (currently Ilse Bracke) to promote the valorisation of research in the IDLab IMEC activities on Internet of Things, 5G and Artificial Intelligence. The primary application domains are smart cities, industry 4.0, mobility and logistics.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Lannoo Bart

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The CONAMO project aims at improving both the training towards and the experience at mass amateur cycling events by continously monitoring and analysing the stream of cycling sensor data generated by the rider and his friends. It does this by introducing both innovations at the network (long-range networks) and the data analysis (machine learning and medical feedback).

Researcher(s)

• Promoter: Latré Steven
• Co-promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of the project is to integrate three complementary, low power wireless technologies. This combination makes energy efficient, multimodal Internet-of -Things communications possible to mobile applications that typically do not stay in the same environment.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Wireless sensor networks (WSNs) have become a very popular concept throughout the last decade, due to their wide applicability in monitoring and control applications (e.g. traffic control, environmental monitoring). They are composed of low-cost, battery-powered, constrained and failure-prone sensors and actuators, which are densely, randomly and redundantly deployed, communicating using wireless radio technologies. Recently, the concept of virtualization was proposed for WSNs. It has been applied to facilitate the creation of virtual sensors that provide more meaningful information by combining readings of multiple physical sensors and to support multi-tenancy and sensor hardware reuse by collocating multiple virtual onto a single physical sensor. The goal of this project is to uncover other, unexplored, benefits of WSN virtualization. Concretely, we will develop fully distributed solutions that allow physical sensors and actuators to self-organize into highly resilient and energy-efficient virtual sensing platforms. Resilience will be provided by exploiting redundancy of sensing hardware and network functions within virtual sensors. Energy-efficiency will be improved by intelligently disabling redundant functionality. Optimizing this trade-off dynamically is the main driver of this project proposal. The first two phases will respectively study control aspects within and between virtual sensors. The third phase will extend the solutions to the highly challenging and mostly unexplored field of mobile sensors.

Researcher(s)

• Promoter: Famaey Jeroen
• Co-promoter: Latré Steven
• Fellow: Mennes Ruben

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

As everyday devices are being connected to the Internet, research on large-scale wireless sensors networks specifically and Internet of Things (IoT) generally are becoming more and more important. There is a considerable research and innovation effort related to the deployment of smart cities using this IoT technology. However, there are still plenty of hurdles to move from R&D to implementation and real mass-scale deployment of wireless sensors networks. Moreover, the city itself is a treasure of data to be explored if the right sensors can be installed. Testbeds are the preferred tools for academic and industrial researchers to evaluate their research but a large-scale multi-technology smart city research infrastructure is currently the missing link. The City of Things research infrastructure will build a multi-technology and multi-level testbed in the city of Antwerp. As a result, 100 locations around the city of Antwerp and its harbour will be equipped with gateways supporting multiple wireless IoT protocols. These gateways will connect with hundreds of wireless sensors and actuators, measuring smart city parameters such as traffic flows, noise, air pollution, etc.

Researcher(s)

• Promoter: Blondia Chris
• Co-promoter: Blust Ronny
• Co-promoter: De Backer Charlotte
• Co-promoter: Goethals Bart
• Co-promoter: Hellinckx Peter
• Co-promoter: Latré Steven
• Co-promoter: Poels Karolien
• Co-promoter: Samson Roeland
• Co-promoter: Vandebosch Heidi
• Co-promoter: Vanelslander Thierry
• Co-promoter: Walrave Michel
• Co-promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Traditional social organizations such as those for the management of healthcare are the result of designs that matched well with an operational context considerably different from the one we are experiencing today. The new context reveals all the fragility of our societies. Main aim of SELFSERV project is to complement the "old recipes" with smarter forms of social organization based on the self-service paradigm and by exploring culture-specific aspects and technological challenges. The SELFSERV project's primary objective is to design a platform addressing the needs of the Moroccan diabetic patients and hooking into the selfserving "wells" of the Moroccan societies and culture.

Researcher(s)

• Promoter: Blondia Chris
• Co-promoter: De Florio Vincenzo

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Traditionally, the Internet was designed as a global communication network, allowing computers to exchange information. In the last decade, other types of devices, such as smartphones, have started connecting to the Internet. Currently, we are at the brink of another evolution, called the Internet of Things (IoT). It envisions connecting everyday appliances and devices to the Internet, such as washing machines, heart rate sensors and traffic lights. This evolution paves the way for society-benefiting applications, such as traffic lights that minimize car waiting time and remote monitoring of heart patients. However, connecting all these devices to the public Internet also carries great risk, as users with malicious intent can hack them to retrieve private information, or even worse, take control of them. To prevent this, and allow critical applications to be safely used in the IoT, the (often wireless) network that connects these devices to the Internet must be secured against hacking attempts and be made tolerant (i.e., resilient) against failures in case something does go wrong. However, this is a very challenging problem, since wireless communications can be easily intercepted or changed. Moreover, many devices are placed in public locations, making them susceptible to tampering. Finally, many envisioned IoT devices could move around (e.g., smartphones or implanted sensors), which further complicates things. The goal of this project is to make wireless IoT networks more secure and resilient. Three types of problems will be addressed: (i) internal attacks, (ii) external attacks and (iii) network failures. Internal attacks originate from a device that is already part of the network, caused by malicious (e.g., hackers that take control of a device) or selfish (e.g., users saving their own battery at the cost of others) behaviour. External attacks originate from outside the network and include hackers trying to find security holes. Finally, network failures are caused by unintentional errors, such as software bugs or malfunctioning hardware.

Researcher(s)

• Promoter: Famaey Jeroen
• Fellow: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Since the introduction of the first smartphones approximately a decade ago, their popularity has skyrocketed. This leads to an increasing use of IEEE 802.11-based communication, better known as Wi-Fi. As Wi-Fi was originally designed for small-scale home and office environments, it does not scale in face of a growing number of connected devices. There is therefore a need for a large-scale, adaptive Wi-Fi MAC and management framework that can provide the necessary QoS guarantees in the face of highly dynamic environments and is at the same time still compatible with limited scale deployments. Within this research project, we will investigate to what extent we can offer the same QoS in large-scale Wi-Fi networks as can be observed in current limited scale Wi-Fi networks. Large-scale networks in this context can range from hundreds up to ten thousands of connected devices. We will do this by introducing a programmable and adaptive (Software-Defined) way to optimise the MAC layer that support: (i) Application-aware load-balancing for dense Wi-Fi deployments (ii) Adaptive control of MAC parameters to support elastic scaling (iii) QoS estimation in challenged Wi-Fi networks (iv) QoS differentiated reservation

Researcher(s)

• Promoter: Latré Steven
• Fellow: Bosch Patrick

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Inventory control has been a popular research topic for a long time. The optimality of various inventory replenishment policies has been studied under a variety of assumptions in demand and lead-time distributions and cost structures. However, its optimality is always studied in a local inventory environment where lead-times are treated exogenously with respect to the replenishment policy. This assumption, however, does not hold in integrated production/inventory systems, where the replenishment policy of the inventory control generates orders that load the production facility. In a finite capacity production environment, the lead-times are load dependent and affected by the current size of the order queue in the production system. In previous research we have studied the impact of traditional replenishment rules in production/inventory systems and we have shown how the inclusion of endogenous lead-times many lead to higher costs. In the proposed research project we aim to propose a class of replenishment policies that are different from the traditional policies and perform better in a production/inventory environment.

Researcher(s)

• Promoter: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

AGILE (Adaptive Gateways for dIverse muLtiple Environments) builds a modular hardware and software gateway for the Internet of Things with support for protocol interoperability, device and data management, IoT apps execution, and external Cloud communication, featuring diverse pilot activities, Open Calls & Community building. AGILE builds a modular and adaptive gateway for IoT devices. Modularity at the hardware level provides support for various wireless and wired IoT networking technologies (e.g. KNX, ZWave, ZigBee, Bluetooth Low Energy, etc.) and allows fast prototyping of IoT solutions for various domains (e.g. home automation, environment monitoring, wearables, etc.). At the software level, different components enable new features: data collection and management on the gateway, intuitive interface for device management, visual workflow editor for creating IoT apps with less coding, and an IoT marketplace for installing IoT apps locally. The AGILE software can auto-configure and adapt based on the hardware configuration so that driver installation and configuration is performed automatically. IoT apps are recommended based on hardware setup, reducing the gateway setup and development time significantly.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The High Impact project of IMEC aims at stimulating fundamental research that can benefit in the long term the valorization of the group. Within this project, the following research lines have been funded: - Appdaptive: configuring IoT networks based on application requirements - Participation in the DARPA Spectrum Collaboration challenge - Densenets: SDN-based network management of wireless networks (resulting in the ORCHESTRA framework) - SubWAN: management of new low power wide area wireless networks

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The High Impact project of IMEC aims at stimulating fundamental research that can benefit in the long term the valorization of the group. Within this project, the following research lines have been funded: - Appdaptive: configuring IoT networks based on application requirements - Participation in the DARPA Spectrum Collaboration challenge - Densenets: SDN-based network management of wireless networks (resulting in the ORCHESTRA framework) - SubWAN: management of new low power wide area wireless networks

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The High Impact project of IMEC aims at stimulating fundamental research that can benefit in the long term the valorization of the group. Within this project, the following research lines have been funded: - Appdaptive: configuring IoT networks based on application requirements - Participation in the DARPA Spectrum Collaboration challenge - Densenets: SDN-based network management of wireless networks (resulting in the ORCHESTRA framework) - SubWAN: management of new low power wide area wireless networks

Researcher(s)

• Promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Project with imec funding on the High Impact topic of wireless communication, specific focus on LPWAN (DASH7, Lora, LoraWAN and Sigfox) and localization (mostly signal strength based). Moreover cooperation with other groups by using OCTA fast prototyping tools.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The High Impact project of IMEC aims at stimulating fundamental research that can benefit in the long term the valorization of the group. Within this project, the following research lines have been funded: - Appdaptive: configuring IoT networks based on application requirements - Participation in the DARPA Spectrum Collaboration challenge - Densenets: SDN-based network management of wireless networks (resulting in the ORCHESTRA framework) - SubWAN: management of new low power wide area wireless networks

Researcher(s)

• Promoter: Famaey Jeroen

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project proposal investigates multi-frequency radio tomographic imaging to advance the accuracy of current device-free localization systems and aims to identify the tracked object using influence of human presence on the combination of different frequencies.

Researcher(s)

• Promoter: Weyn Maarten
• Fellow: Denis Stijn

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The DASH7 Alliance Protocol is an evolution of the ISO 18000-7 standard for active RFID using 433 MHz, initially promoted by the US Department of Defense for container inventory. DASH7 targets wireless machine-to-machine communication for low-power, mid-range applications. This project aims to set up a certification lab for the wireless communication standard DASH7 with an accompanying pre-compliance testing system.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The central research question of the SMILE-IT project is: "How can complex networks become self- organizing while ensuring stability and without sacrificing on performance. Moreover the decisions taken by the system should be understand- able and guidable." More precisely, the project aims to develop a framework for studying and managing modern distributed networked systems that contain a large number of entities or agents, both machine and human, which strive to achieve their personal objectives. The framework developed within the proposal will guide these entities, either through direct control or by way of incentives, in order to achieve system-wide optimal behaviour, satisfy global objectives and adhere to the system's operational constraints in the face of diverging and incompatible personal goals. Software language abstractions will be identified and developed, to support the ease of the deployment of the framework on a wide variety of networks. The framework will build on the expertise of the teams in machine learning (including game theory, self- organization of complex systems, large-scale multi-agent systems and emergent social behaviour), network management and modelling, and software language design. The key idea of the framework is that the context within which intelligent decision making components or agents operate may depend on spatial and temporal factors. As such, they should be able to adapt their behaviour and goals as a function of space and time. The framework should satisfy the following requirements: It should be generic so as to be applicable to a wide range of networks, it should be scalable with respect to the size of the network, the resulting behaviour should be (near) optimal and at all times minimal performance should be guaranteed, also in unexpected situations. Several fundamental scientific challenges remain to be solved before this high-level objective can be achieved. They can be summarized as follows: - Complex multi-agent control: The SMILE-IT project will develop programming abstractions for distributed network control, that allow agents to be configured and controlled in a network-, rather than agent-centric manner. Moreover, it will provide abstractions to efficiently query and control the state of large-scale complex networks, as reinforcement learning techniques continuously require a view on the current state of the environment. - Fast and stable convergence towards an acceptable solution: SMILE-IT aims to guarantee acceptable performance as soon as the management agents go operational. The project will investigate how to combine learning with heuristic knowledge and existing control strategies, in order to guarantee performance during learning. Moreover, solutions are needed that allow agents with (partially) conflicting goals to collaborate and jointly achieve a converged policy that leads to acceptable performance for all. - Robust and adaptive management under unexpected conditions: Novel learning-based techniques that can cope with large degrees of uncertainty, and select suitable actions even if the network's state is only partially known will be developed. Moreover, unexpected situations, such as failures or faults, may occur during operations. SMILE-IT will investigate how reinforcement learning techniques can be applied to detecting and recovering from such unexpected situations. The development of the SMILE-IT framework will be guided by 2 driving cases in the smart grids and telecom networks application domains. Advanced prototypes for these cases will be developed to support an extensive evaluation of the SMILE-IT technologies. In a later phase, applications to a number of additional domains proposed by the user committee (such as traffic networks) will be examined, in order to demonstrate the general applicability of SMILE-IT methods.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project represents a research agreement between the UA and on the onther hand IWT. UA provides IWT research results mentioned in the title of the project under the conditions as stipulated in this contract.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of this research project is to redesign the way services are delivered across the Internet. Instead of focusing on traditional host-based schemes (e.g., TCP/IP-based transport) and best effort delivery models across autonomic network domains, we will investigate how we can exploit both network and cloud virtualization techniques to provide end-to-end QoS guarantees on the service delivery.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Optimal operation of sensor networks takes on a fundamentally different nature when energy harvesting is a primary or even secondary means of power supply due to the following reasons: (1) the topology of active nodes may change more drastically due to intermittency in the energy supply. This necessitates an even greater focus on resilience and self-organization in the control and performance of such networks; (2) the unpredictable nature of energy supply represents not only a challenge but also an opportunity: not only is it important to be frugal when energy supply is low, good controls for such networks should also make good use of periods or regions with high energy supply. Therefore, our objectives are to assess how these differences affect the performance of such networks by mathematical models, and also, inspired and informed by these models, to propose new control principles for such networks.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The WiSHFUL project (Wireless Software and Hardware platforms for Flexible and Unified radio and network controL) will reduce the threshold for experimentation in view of wireless innovation creation and by increasing the realism of experimentation. More specifically, the WiSHFUL objectives are: To offer open, flexible & adaptive software and hardware platforms for radio control and network protocol development allowing rapid prototyping of innovative end-to-end wireless solutions and systems in different vertical markets (manufacturing, smart cities, home, office, healthcare, transportation, logistics, environmental monitoring...). Key features of such platforms are: Unified radio control allowing full radio control without the need for deep knowledge of the hardware specifics of the radio hardware platform; Unified network control, allowing rapid prototyping and adaptations of network protocol stacks, without the need for deep knowledge of network protocols and software architectures, but also allowing the implementation of novel protocols (e.g. cooperative protocols which require time synchronization and coordination of a subset of nodes); Support for experimentation with intelligent control of radio and network settings, enabling intelligent, node-level and network-wide decisions, on radio and network operation modes and according settings, driven by higher-level domain-specific application demands and taking into account external policies (for example policies for dynamic spectrum access). To offer advanced wireless test facilities that: follow the current de facto standards in FIRE, set by the FED4FIRE project, for testbed interoperability adopting and extending standardized tools for discovery and reservation, experiment control, measurements & monitoring supporting federated identity management and access control; To support diverse wireless (access) technologies and platforms: Create generic and open interfaces for control of the existing devices for technologies like Wi-Fi (IEEE 802.11), Bluetooth (IEEE 802.15.1), WPAN (IEEE 802.15.4), LTE, WiMAX that are already available in current facilities; Extend these interfaces to more open ended experimental radio platforms covering software defined radio platforms, embedded devices and non-commercial grade hardware, so as to enable 5G, Internet of Things (IoT), Machine-to-Machine (M2M), tactile internet; To offer portable facilities that can be deployed at any location allowing validation of innovative wireless solutions in the real world (with realistic channel propagation and interference characteristics) and involving real users. To extend the WiSHFUL facilities with additional facilities or wireless hardware, offering complementary or novel radio hardware/software platforms, supporting experimentation with new technologies such as mmWave (WiGig 60GHz and IEEE802.11ad), full duplex radio, IoT testbeds, smart antennas, etc. To attract and support experimenters for wireless innovation creation targeting different classes of experimenters via different open call mechanisms tailored to the specific classes (industrial relevance for SME versus level of innovation for academia).

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

SHIFT-TV has the ambitious goal of defining the next generation IPTV architecture which completely obsoletes current IPTV systems, while at the same time offering a superior experience compared to OTT (over-the-top) video delivery mechanisms such as adaptive bit rate streaming.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The iMinds IoT program aims at creating a leap forward in Flanders with respect to IoT by consolidating all researchers on one physical location in Ghent. Different research tracks have been defined in the domain of IoT. Within this program, the research group PATS is active in the Cloudlet research line, where we investigate how to develop AI algorithms on top of resource constrained IoT devices.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of the iFEST project is to improve the digital experience at large events such as festivals by developing a new generation of bracelets, which will be integrated with advanced communication and sensor capabilities. Moreover, the necessary festival software platform to both manage the bracelets and analyse the data they generate will be designed. This provides an answer to the "analog way" festival organizers and festivalgoers still experience a festival (i.e., limitedc interaction & communication mechanisms) and will help the market of live entertainment to maintain its strong position within the entertainment sector.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of the iFEST project is to improve the digital experience at large events such as festivals by developing a new generation of bracelets, which will be integrated with advanced communication and sensor capabilities. Moreover, the necessary festival software platform to both manage the bracelets and analyse the data they generate will be designed. This provides an answer to the "analog way" festival organizers and festivalgoers still experience a festival (i.e., limitedc interaction & communication mechanisms) and will help the market of live entertainment to maintain its strong position within the entertainment sector.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The aim of EMD is to research and demonstrate the core technologies of a media distribution platform that operates across public / private networks with different characteristics and capable of supporting high quality / low latency video collaboration applications in a secure yet user friendly manner.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The Internet has experienced an important evolution since its invention several decades ago. Started as a simple packet forwarder, it is now a delivery platform for rich and demanding services such as cloud applications and video streaming. Despite this important evolution, its underlying architecture has remained the same over the years. Because of this, the Internet lacks flexibility: it is still not possible to provide Quality of Service (QoS) guarantees to Internet services offered by third parties (called Over The Top services) such as Google Apps or Skype. This often leads to severe degradations in the overall quality of the service and corresponding customer satisfaction. A second important evolution is the increasing popularity of cloud environments and their continuous integration in todays Internet. As a result, there is no longer "a cloud" or "an Internet": the two are converged towards one unified platform. In designing the future Internet, it is therefore important to consider the management of a unified cloud and network environment. Because of these evolutions, the research community has been investigating ways of programmatically managing the network (called Software Defined Networking) and virtualizing network resources. Within these activities, the goal is to achieve the same flexibility as can be reached in a cloud environment. Within the area of network virtualization, the concept of Service Function Chains (SFCs) plays an important role. SFCs are graphs, consisting of different sub-services (e.g., a video streamer, a part of a cloud application), which can be distributed and deployed across multiple datacenters and jointly form the Internet service. The construction of these SFCs and the assigning (called embedding) of the SFC components to the different datacenters in a scalable way is an important unsolved problem. The goal of this PhD project is to develop algorithms to construct and embed such SFCs and adapt them dynamically based on user mobility, variable service requirements, and network dynamics. Because of these changes, the optimal SFC construction changes and an on-the-fly provisioning of resources is required. We will consider both optimal algorithms using mathematical optimization techniques and approximations using multi-agent heuristics. By enabling a scalable and dynamic construction and embedding of SFCs, it becomes possible to offer Over The Top services with the necessary QoS guarantees.

Researcher(s)

• Promoter: Latré Steven
• Fellow: Spinnewyn Bart

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The BeamSat project fits in the development of Newtec's next generation satellite broadband access system using multi-spotbeam Ka-band technology and wideband bent-pipe RF channels. The purpose of BeamSat is to define and develop the next generation broadband Sat3Play solution in order to accelerate Newtec's growth within the broadband market.

Researcher(s)

• Promoter: Latré Steven

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The purpose of this research is transferring the virtualization technique form general purpose systems to embedded systems, together with the multi-core technology on embedded systems makes it very interesting. Virtualization makes it possible to execute multiple software components on the same hardware, in an isolated and secure way. An important characteristic of embedded systems is the hard real-time behavior. We must continue to insure this behavior when we apply the virtualization on embedded multi-core systems.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Broeckhove Jan
• Fellow: De Bock Yorick

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objective of this project is to design and to analyze the performance of garbage collection algorithms for flash-based solid state drives that make use of page-, block- or hybrid-mapped flash translation layers. The analysis will mainly focus on the impact of the garbage collection algorithm on the write performance and lifespan of the solid state drive, while different data models, data separation techniques and the use of the TRIM command will be considered.

Researcher(s)

• Promoter: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Our objective is to derive closed-form results and/or fast numerical procedures to assess the main performance characteristics of the proposed queueing models, such as throughput, customer delay, system content, loss characteristics, etc., and to apply these results in a number of relevant areas such as Cognitive Radio, Medium Access Control and server farms.

Researcher(s)

• Promoter: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Bringing the images from camera to TV screen is a complex production process involving several actors in the media sector. As currently no decent technological solution is available to connect geographically spread locations, many media actors are located close to each other, in order to allow efficient collaboration. The MECaNO project addresses these limitations by developing a network technology enabling decentralised collaboration, just-in-time workflows and guaranteed performance. As such, MECaNO will deliver both a technological and techno-economic optimal solution for the media sector, and evaluate it on realistic use-cases.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In order to guarantee quality-of-service, the wireless network spectrum has been licensed in a static manner. This has resulted in a substantial amount of underutilization in some these licensed frequency bands. With the ever-increasing demand for wireless bandwidth, secondary users are now being introduced (using cognitive radios) to take advantage of the remaining free space on these frequency bands. Within this project multiple access algorithms to coordinate the transmissions of these secondary users are developed and analyzed (using analytical methods). This problem is fundamentally different from the traditional (multi-channel) multiple access problem due to the presence of the primary users. The project focuses to a large extent on algorithms with free access (allowing users to join in without difficulty) and tree algorithm to resolve possible conflicts. The main performance measures of interest include the maximum stable throughput, the resulting delay characteristics and the energy consumption of these algorithms.

Researcher(s)

• Promoter: Van Houdt Benny
• Fellow: Block Robbe

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project represents a formal research agreement between UA and on the other hand MMS. UA provides MMS research results mentioned in the title of the project under the conditions as stipulated in this contract.

Researcher(s)

• Promoter: Weyn Maarten

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of this project is to develop efficient computational techniques for the study of patterns of chemicals in cell-based plant simulations. In particular I study numerical methods for the bifurcation analysis and parameter dependence of such patterns in transport equations of morphogens such as auxin. I focus on the application of iterative methods and development of preconditioners to solve the large, sparse systems of equations that underpin the biological models. Additionally I design data structures and parallel algorithms to implement the above methods. This will allow studies of patterns in transport equations in large 3D plant tissues with massive numbers of cells. Maintaining scalability on modern parallel computers is an important factor. This research will push current methods in this field to the next level w.r.t. efficiency, scale of simulation and understanding of dynamics of morphogen patterns.

Researcher(s)

• Promoter: Klosiewicz Przemyslaw

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Power industry markets are currently at the forefront of a disruptive change, driven by new developments in decentralized energy production and in consumer demand. These developments make it significantly more difficult to balance demand and supply, but also lead to increased potential for and value of demand response schemes. Incentivizing the participation in such schemes can be achieved by dynamically changing end-user prices for power consumption based on the temporal and spatial properties of supply and demand. Within this context there is an increasing need for fundamental research that deals with challenges related to optimally aligning consumption of electricity and the delivery of electricity to the distribution network. The objective of our project is to advance the state-of-the-art in scheduling algorithms that realize such a cost-efficient alignment. We thereby take into account the capacity of local energy production and storage facilities, as well as the dynamically evolving cost for consuming energy and injecting energy into the distribution grid. This involves solving a large scale and complex scheduling problem under uncertainty, as well as the design of a mechanism that allows for simple and correct preference elicitation from end users.

Researcher(s)

• Promoter: Vanmechelen Kurt

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The aim of this project is to develop a fully integrated and customized cross-layer optimization framework with real-time adaptive DSM for 4GBB, that optimally leverages the network capabilities so as to deliver an optimized QoS and that can eventually serve as a base for broadband access technology for the coming decades.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Many elderly citizens, even though affected by chronic disabilities, wish to retain their autonomy and enjoy their own home for as long as possible. This determines the need for Electronics and ICT systems capable of detecting alarming situations that require intervention, or collecting data to anticipate complications in domestic health care. For professional help this acts as a strong support tool. The Little Sister project will research, implement and demonstrate low-cost autonomous technology to provide protection and assistance to elderly citizens.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The SEMAFOUR project will design and develop a unified self-management system, which enables the network operators to holistically manage and operate their complex heterogeneous mobile networks. The ultimate goal is to create a management system that enables an enhanced quality of user experience, improved network performance, improved manageability, and reduced operational costs.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This research project aims at the construction of adequate stochastic models and/or queueing models for contention-less MAC protocols and the development of corresponding performance analysis techniques. In queueing theoretic terms, we expect to be confronted with queueing models with priority scheduling, multiple servers, server vacations, correlated arrivals and queues in tandem. Performance measures of interest are the packet delay, packet loss ratios, the response time and the energy consumption.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project represents a research agreement between the UA and on the onther hand IWT. UA provides IWT research results mentioned in the title of the project under the conditions as stipulated in this contract.

Researcher(s)

• Promoter: Broeckhove Jan
• Fellow: Stijven Sean

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The purpose of the proposed project is to develop a cognitive radio/networking solution for wireless data services that can guarantee quality of service. The approach we take in this proposal is to introduce spectrum sensing, the most basic function of future cognitive radios and cognitive networking to solve the spectrum bottleneck and mitigate interference.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main research objectives of the RAILS-project are: - on-board and train-to-wayside network optimizations; - railway application support and management platform.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Community networks are an emerging and successful model for the Future Internet across Europe and far beyond.The CONFINE project complements existing FIRE infrastructure by establishing a new facility built on the federation of existing community IP networks constituted by more than 20,000 nodes and 20,000 Km of links. These community networks incorporate a large and wide variety of commodity wireless and optical links, heterogeneous nodes, different routing protocols, applications and a large number of end-users, following an innovative model of self-provisioning using unlicensed and public spectrum.The project develops a unified access to an open testbed with tools that allow researchers to deploy, run, monitor and experiment with services, protocols and applications on real-world community IP networks. This integrated platform will provide user-friendly access to these emerging networks supporting any stakeholder interested in developing and testing experimental technologies for open and interoperable network infrastructures, strengthening open community networks. This type of networks is an emerging and successful model for the Future Internet across Europe and beyond. The project includes as partners well established community networks with large end-user bases and diverse application providers (e.g. content distribution, voice, data and multimedia communication), research institutions with experience in key related areas, non-profit organizations and SMEs with experience in supporting researchers, community networks and end-users.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This research is situated in the field of telecommunication systems and more specifically that of wireless sensor networks. The main focus of this project is to investigate mechanisms that introduce the MAC-layer interoperability needed to allow cross-network cooperation between sensor networks.

Researcher(s)

• Promoter: Blondia Chris
• Fellow: van den Akker Daniel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In order to guarantee quality-of-service, the wireless network spectrum has been licensed in a static manner. This has resulted in a substantial amount of underutilization in some these licensed frequency bands. With the ever-increasing demand for wireless bandwidth, secondary users are now being introduced (using cognitive radios) to take advantage of the remaining free space on these frequency bands. Within this project multiple access algorithms to coordinate the transmissions of these secondary users are developed and analyzed (using analytical methods). This problem is fundamentally different from the traditional (multi-channel) multiple access problem due to the presence of the primary users. The project focuses to a large extent on algorithms with free access (allowing users to join in without difficulty) and tree algorithm to resolve possible conflicts. The main performance measures of interest include the maximum stable throughput, the resulting delay characteristics and the energy consumption of these algorithms.

Researcher(s)

• Promoter: Van Houdt Benny
• Fellow: Block Robbe

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

By realising this project MoTuM will be the first AGV supplier offering a decentralized AGV control platform. MoTuM will also be the first using a Wireless Mesh Network for AGV applications. The result will be a platform that allows system integrators to install and commission efficiently MoTuM's AGV systems.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The BeamSat project is an ESA-ARTES 5.2 project that entails the development of Newtec's third generation satellite broadband access system using multi-spotbeam KA-band technology and wideband transparent transponders. In BeamSat 1d, a traffic modeling tool will be developed that will generate traffic statistics based on the network configuration, a traffic model for the services provided in the network and the user SLA's.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Within the GreenWeCan project (Green Wireless Efficient City Access Network), a "green" dual wireless city access network infrastructure, consisting of a wireless local area network (WLAN) and a wireless sensor network (WSN), will be investigated. Both network parts will be able to offer innovative services integrated into several geospatial applications by aggregating data from multiple sources. Much attention will be paid to "green" aspects: human exposure to radiation and energy consumption savings. To validate and analyse this, a demonstrator network will be built.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The objective of this projectis to do the necessary research and prototyping to be able to develop a reliable predictable transport network based on Carrier Ethernet technology using MPLS-TP.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The introduction of economic principles in Grid computing, Grid Economics, has two separate meanings. On the one hand the use of economically inspired principles to develop effective forms of resource management, and on the other hand to enable supplying resources and services as an economic activity. The fact that a substantial number of Grid applications is very data intensive has led to the research and propositions of scheduling algorithms that take into account the effects of data transport, so called network aware scheduling. An approach like this can both increase efficiency of computational as well as network resources and decrease responsetimes for jobs. In this project, we aim to combine Grid economics and network aware scheduling. The objective is the design of algorithms and protocols that allow the co-allocation of network and computational resources when using a grid resource management system based on economic markets. With regards to co-allocation, already a lot of work has been done. On a more limited scale research is being done on the use of market mechanisms for the allocation of network paths. The combination of a market and co-allocation of network and computational resources has not been explored before however. This combination leads to new possibilities when creating allocations and schedules. It creates a more relevant description of the value a user associates with a certain allocation. Because of this, economic and network aware scheduling will be a unique contribution to the domain of Grid economics. This project also fits nicely into the current research being done in the CoMP group into different market mechanisms and their applicability in economic scheduling of computational resources in grids.

Researcher(s)

• Promoter: Broeckhove Jan
• Fellow: Depoorter Wim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The project includes the development of a generally applicable Software Development Kit (SDK) for the integration of digital signatures. This SDK is composed of multiple components as there are a client signing applet, a server verification component and plug in components. The SDK will serve as a basis to offer through a spin-off of the University Antwerp the following services: on line document signing, strong authentication, on line contract negotiations and smart card systems.

Researcher(s)

• Promoter: Broeckhove Jan
• Co-promoter: Demeyer Serge
• Co-promoter: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

For traditional optical networks various network dimensioning techniques exist to determine the required amount of network resources. Grid users however are not concerned with the exact location of the server that executes their job. As a result, the destination is not known a priori, which implies that traditional solutions cannot be applied directly. Within this project we wish to address this problem using Mean Field models and relaxation techniques for Integer Linear Programming problems.

Researcher(s)

• Promoter: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The BeamSat project consists of a technology development phase and a product development phase for Newtec's third generation IP satellite access network. The functional high level architecture of such a system is known and the components of the Next Generation (Third generation) system will be similar to the existing components of the heritage broadband system Sat3Play.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The OMUS project aims at optimizing multimedia services, esp. video streaming, in both a professional conferencing context and residential (video) services on multiple levels: (i) local network technologies (esp. 802.11n), (ii) video coding and quality monitoring, (iii) peer-to-peer technologies for content delivery.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Because of the strong increase of advanced multimedia applications requiring high bit rates, the capacity of the current wireless networks becomes insufficient. Further, the increase of the mobility in time-variable radio channels and the higher bit rates cause an increasing frequency selectivity, such that the transmitted signals are distorted, and disturbed by interference and noise. It is therefore of great importance to investigate new techniques to increase the capacity (in terms of more users and higher bit rates) and to improve the performance for the transmission over these radio channels.In this project proposal, the expertise of the different research groups will be brought together in order to obtain research results that lead to an increase of the capacity of future wireless networks.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The adoption of economic principles for resource management in grids can lead to a significant increase in the efficiency under which grid systems operate. In this project, we remove a number of barriers to the integration of market-based resource management in grids through the introduction of advanced task, resource and valuation models, and through the development of bidding support tools.

Researcher(s)

• Promoter: Vanmechelen Kurt

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The TRACK project aims to design a novel and future-proof network and application architecture and corresponding network and application solutions that can cope with the dynamic wireless train environment (such as the intermittent availability of heterogeneous wireless access networks and the dynamics of individual wireless links) and that is capable to deal with the stringent QoS requirements of low-latency and/or safety critical train applications such as train control & diagnostics, real time security (CCTV video surveillance), energy management, real-time PIS and crew communications.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The purpose of this project is to define principles for algorithms and protocols related to medium access control, routing and resource allocation in wireless multimedia sensor networks. These allow for QoS provisioning to be supported, taking into account the characteristics of both multimedia streams and wireless sensor networks. These mechanisms are distributed, scalable and adapt to the dynamics the network.

Researcher(s)

• Promoter: Blondia Chris
• Fellow: van den Akker Daniel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This is a fundamental research project financed by the Research Foundation - Flanders (FWO). The project was subsidized after selection by the FWO-expert panel.

Researcher(s)

• Promoter: Dhaene Tom
• Co-promoter: Broeckhove Jan
• Fellow: Crombecq Karel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objective of the project is to design a global architecture and to develop networking solutions and service enablers to support advanced cross-layerlcross-network cooperation between independent co-located wired & wireless, homogeneous & heterogeneous networks. The ultimate goal is to demonstrate these Symbiotic Networking concepts in a real-life home/office environment.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The SOCRATES project investigates the application of self-organisation methods, which includes mechanisms for self-optimisation, self-configuration and self-healing, as a promising opportunity to automate wireless access network planning and optimisation, thus reducing substantially the Operational Expenditure (OPEX) and improving network coverage, resource utilisation and service quality. Fundamental drivers for the deployment of self-organisation methods are the complexity of the contemporary heterogeneous access network technologies, the growing diversity in offered services and the need for enhanced competitiveness.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The introduction of economic principles in Grid computing, Grid Economics, has two separate meanings. On the one hand the use of economically inspired principles to develop effective forms of resource management, and on the other hand to enable supplying resources and services as an economic activity. The fact that a substantial number of Grid applications is very data intensive has led to the research and propositions of scheduling algorithms that take into account the effects of data transport, so called network aware scheduling. An approach like this can both increase efficiency of computational as well as network resources and decrease responsetimes for jobs. In this project, we aim to combine Grid economics and network aware scheduling. The objective is the design of algorithms and protocols that allow the co-allocation of network and computational resources when using a grid resource management system based on economic markets. With regards to co-allocation, already a lot of work has been done. On a more limited scale research is being done on the use of market mechanisms for the allocation of network paths. The combination of a market and co-allocation of network and computational resources has not been explored before however. This combination leads to new possibilities when creating allocations and schedules. It creates a more relevant description of the value a user associates with a certain allocation. Because of this, economic and network aware scheduling will be a unique contribution to the domain of Grid economics. This project also fits nicely into the current research being done in the CoMP group into different market mechanisms and their applicability in economic scheduling of computational resources in grids.

Researcher(s)

• Promoter: Broeckhove Jan
• Fellow: Depoorter Wim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project proposal reflects the intention of some of the most prominent players in the Belgian ICT sector to cooperate with research institutes and governments to develop and demonstrate a number of ITS services (ITS: Intelligent Transport Services). The different applications will be based on European standards to ensure interoperability between different market players and across geographical borders. Furthermore specific research will be done related to the integration of the different applications on a aeneric multi-application platform.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project aims at: -Designing and building Proof of Concepts (POCs) of a high Definition media production and storage environment, based on an optimal choice of mostly IP based components. The project will cover research in the area of low end iSCSI storage, IP based SANs and disk connection networks, TCP/IP and iSCSI offload engines (TOE's), IPoIP (IP over InfiniBand) and iSER as a possible alternative to TOE's, different CPU technologies (AMD vs Intel), GPFS, GRID computing, cell-processors, alternative secondary storage concepts and new backup/restore paradigms; -Studying new media formats, follow-up of standardization and evolution of MXF and AAF, Service Oriented Archtiecture (SOA), Enterprise Service Bus (ESB); -Investigating the relevance of generic GRID computing concepts for the GPFS cluster based media infrastructure models.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris
• Promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main aim of this project is to introduce new computational tools, based on the External Complex Scaling methodology developed in atomic and molecular physics, for microscopic scattering and reaction calculations in cluster models for light nuclei.

Researcher(s)

• Promoter: Arickx Frans
• Promoter: Broeckhove Jan
• Co-promoter: Arickx Frans
• Co-promoter: Broeckhove Jan
• Co-promoter: Vanroose Wim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objective of the project is to study key challenges in the creation of a cross-layer WSN framework that optimally exploits modularity of and cross-layer interactions between the different building blocks to this framework in order to support heterogeneity, scalability, energy-efficiency, QoS and mobility. For the different modules within the cross-layer framework we have the following research objectives. -To design and analyze generic, resource-aware and QoS-aware modular Medium Access Control (MAC) protocols and scalable modular networking solutions for intra-and inter-WSN communications (merging WSN) -To analyse security weaknesses in WSNs through an adversary model, to evaluate network and middleware solutions in terms of security and to adapt these solutions in order to make them more resistant against will-know and new types of security attacks. -To develop coordination mechanisms and middleware support that is based on multi-agent technologies. The services to be integrated in the multi-agent systems include synchronization, localization and data aggregation -To develop adequate gateway functionalities needed for communication of WSNs with IP-based networks.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objective of the DEUS project is to develop wireless network solutions & service platforms for easy deployment of wireless network infrastructures and easy set-up of location-aware services in dynamic environments.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris
• Fellow: Van de Velde Erwin

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Parameterized computer experiments and simulations become more and more important in the design of complex systems. The main goal of this project is to improve the current, hard-to-handle and hard to configure adaptive surrogate modelling techniques, approaching the problem from a theoretical as well as a practical (computational) standpoint. This will result in a generic, widely applicable modelling algorithm that configures and adapts itself as good as possible to the problem at hand, instead of relying on input from the user. A framework will be investigated and designed that will utilize this algorithm to automatically generate compact, scalable surrogate models for complex, dynamic systems, based on a limited amount of intelligently chosen sample points.

Researcher(s)

• Promoter: Dhaene Tom
• Co-promoter: Broeckhove Jan
• Fellow: Crombecq Karel

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

The introduction of economic principles in Grid computing, Grid Economics, has two separate meanings. On the one hand the use of economically inspired principles to develop effective forms of resource management, and on the other hand to enable supplying resources and services as an economic activity. The fact that a substantial number of Grid applications is very data intensive has led to the research and propositions of scheduling algorithms that take into account the effects of data transport, so called network aware scheduling. An approach like this can both increase efficiency of computational as well as network resources and decrease responsetimes for jobs. In this project, we aim to combine Grid economics and network aware scheduling. The objective is the design of algorithms and protocols that allow the co-allocation of network and computational resources when using a grid resource management system based on economic markets. With regards to co-allocation, already a lot of work has been done. On a more limited scale research is being done on the use of market mechanisms for the allocation of network paths. The combination of a market and co-allocation of network and computational resources has not been explored before however. This combination leads to new possibilities when creating allocations and schedules. It creates a more relevant description of the value a user associates with a certain allocation. Because of this, economic and network aware scheduling will be a unique contribution to the domain of Grid economics. This project also fits nicely into the current research being done in the CoMP group into different market mechanisms and their applicability in economic scheduling of computational resources in grids.

Researcher(s)

• Promoter: Broeckhove Jan
• Fellow: Depoorter Wim

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

A large part of the time-to-market of an Integrated Circuit (IC) is the time needed to design the IC. The design time is, in its turn, to a large extent dominated by the time needed to properly simulate the design. As the simulation time is growing instead of decreasing with each new IC technology, methods must be found to reduce simulation time. One class of such methods covers the so-called model reduction methods. These methods aim to simplify a given IC model such that the simplified model requires less evaluation time but preserves the essential behaviour of the original IC model. The overall objective of the project is to develop such model reduction methods.

Researcher(s)

• Promoter: Dhaene Tom

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objectives of the proposed project are the derivation of analytic or semi-analytic solutions for adequate stochastic models of optical buffers and switches, allowing fast and efficient calculation of various performance measures, such as packet loss, mean delay, delay jitter, etcetera. Ultimately, these measures quantify the QoS (Quality of Service) the users of the network will experience, and, as such, are of prime importance to network designers and providers.

Researcher(s)

• Promoter: Blondia Chris
• Co-promoter: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

ADAMO's primary goal is to specify, research and design a demonstrator for this disaster management architecture where not only those 'in the field' but also the decision makers in the backoffice or crisis-center get a real-time view on the full deployment of a disaster.ADAMO will be able to inform all members of the intervention-chain through continuous questioning and through pushing information to the right people at the right time.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In this project we investigate the optimization on a Grid platform of the rendering process for the three-dimensional animations. We will use the BEgrid for actual development and deployement. We will perform a thorough analysis of the gridenablement of the rendering application. We will also consider in detail which factors determine the performance gains resulting from the distribution of the application onto the grid platform.

Researcher(s)

• Promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main objective of the EURO-FGI network is to develop and maintain the most prominent European Center of Excellence in Future Generation Internet (FGI) design and engineering, acting as a Collective Intelligence Think Tank, representing a major support for the European industry and leading towards a European leadership in this domain.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Dhaene Tom
• Fellow: Crombecq Karel

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The end-goal of this project is to specify, to research and to design a demonstrator of a mobile platform (targeted towards cars, busses, trucks etc.) which, through different networks, always keeps the best possible connection between: 1) the vehicle and it's backend infrastructure 2) between vehicles themselves (ad-hoc / mesh)

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

This project aims at developing a series of novel stochastic models to assess the performance of telecommunication systems. The emphasis lies on setting up efficient computational procedures for each of these models. Applications are situated in the area of cable (DOCSIS) access networks, optical switches/buffers, the backbone Internet, etc.

Researcher(s)

• Promoter: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of this research project is to develop univariate and multivariate sampling and modeling algorithms, based on rational and polynomial interpolants, for efficient design-space exploration of general linear dynamic systems, such as passive electronic microwave and RF systems. This reflective exploration technique must sample the (one- or multidimensional) parameter space in an optimal way in order to minimize the number of samples, without assuming any prior knowledge of the system.

Researcher(s)

• Promoter: Dhaene Tom

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

We plan to extend the microscopic model for the quantum analysis of scattering of nuclei with a large excess of protons or neutrons, to include dynamical polarization ¿ i.e. deformation of the nucleus during the scattering process. Application of this approach to two- and three-cluster reactions in light nuclei is key to for certain astrophysical applications.

Researcher(s)

• Promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Different existing routing algorithms will be compared with special attention for throughput scalability and the speed of the setup of new connections. This will happen with the focus on the application of ad hoc networks in disaster scenarios. Apart from the comparison of existing routing algorithms research will be done on new routing algorithms that not only enhance the throughput, scalability and speed of connection setup, but can also guarantee quality of service for e.g. videostreams.

Researcher(s)

• Promoter: Blondia Chris
• Fellow: Van de Velde Erwin

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The project main S/T objectives are to apply the most advanced control technologies ( control the- ory, sensory devices, electronic embedded systems, and, in general, ICT) to radically innovate a class of products -industrial robots -where these technologies did not yet find full applications (the demanding missions have mostly been satisfied by heavy, costly, difficult to reconfigure, me- chanical solutions). The chosen class of products is such that the success of the project will have important impacts not only in that specific class, but on all the manufacturing systems (via its dif- fusion and substitution potentialities). The project's ambitious aim is to produce a step change in theindustrial robot design philosophy with respect to the state ofthe art. Today, the intrinsic poten- tial high flexibility and adaptability for different jobs of industrial robots is somewhat hindered by a design philosophy strongly biased on mechanical solutions (to reach demanding specifications such as high precision) than on the electronic and control design potentialities.

Researcher(s)

• Promoter: De Florio Vincenzo

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Wireless technology is a key driver in adding value in building automation through the deployment of technology. Indeed, installing and commissioning a myriad of wired networks has been reported to be a major source of effort and thus of cost. The multitude of wired networks in a typical professional building consists of the computer network, the fire alarm network, the emergency lighting network, the access control network, etc. Recently the different networks are being deployed using a building automation bus system such as EIB, LON and BACNET. Where the interoperability issues for these wired networks are gradually being solved, fundamental technological problems remain when deploying these services over wireless networks. These challenges need to be met simultaneously, adding extra complexity to the task. The innovation objective of this project is to tackle each challenge both individually and as a component of the full framework. The resulting wireless architectures, algorithms and technique will the form an example of cutting edge scientific results on how to deploy wireless building automation. As buildings are a harsh environment for wireless signals, a key element in the project is to build and evaluate a Proof-of-Concept.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

In this project, the use of economic principles and mechanisms as the basis for resource allocation will be investigated. The goal is the development of a, on an econonomical model based, resource manager and job scheduler. By this, metacomputing will gain an economic dimension which allows for solving computational problems by renting resources such as computational power and storage from idle computers.

Researcher(s)

• Promoter: Stuer Gunther

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal of the research project is the development of new heuristics and new software tools for the exploration and optimalisation of the power- and memory consumption of embedded software systems. De new heuristics and tools aim explicitly at a high abstraction level and will enable the exploration of the data structures used within the embedded software system.

Researcher(s)

• Promoter: Dhaene Tom

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The aim of the ACES research network is to group research teams working on different aspects of future computing systems in Flanders and surrounding regions, to give visibility to our domain by yearly organizing a couple of international events with world-class experts in Flanders, and to stimulate collaboration between the different teams.

Researcher(s)

• Promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

Recent developments in intelligent sensors, such as (bio) medic sensors, in combination with the steady evolution in wireless networks, in particular ad hoc networks, pave the way for a new type of networks, Body Area Networks or BAN's. A BAN is a network in which, several compact mobile devices, attached to the body, communicate using a wireless network. Interaction with the user will be possible using a central intelligent device (e.g. a PDA). We distinguish two types of devices: sensors and actuators. Sensors will be used to measure certain parameters. Some examples of sensors are: external medical sensors (monitor the heartbeat, blood pressure, body temperature, recording a long term ECG ...), internal or implanted medical sensors (measuring of the cerebral activity for epileptic patients, measuring the glucose levels in the blood of diabetics, endoscopy where the sensor is built into a pill, retina prostheses for the visually impaired existing of a matrix of micro sensors that transform electrical signals into neurological signals and thus mimic the normal behaviour of the retina ...), microphone, headphone, digital glasses with the functionality of a PC monitor, ... The second category of devices is called actuators that will act in function of the measured parameters in the sensors or by interaction with the user. Some possibilities for actuators are: a device for administering the correct dose of insulin to diabetics, based on the measures glucose level, intravenous administering of medication when a higher cerebral activity is detected and thus preventing an epileptic seizure, changing the image of the digital glasses, ... Thanks to a BAN, in the future it will be possible to monitor patients continuously and to apply the necessary medication, whether it is in a hospital or at home or during transportation. Patients will no longer need to keep to their beds, but will be able to move about freely. Elderly people will be able to live in their own home longer and will not need to move to a retirement home or at least not until a later age. Training schedules of professional athletes can be monitored more closely.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Guaranteed passive broadband circuit models that capture the complex input-output behavior of general multiport passive linear structures are the holy grail in microwave and RF modeling. The macromodels must be compact, fast, numerically stable, physics-based (i.e. causal and passive), and applicable in time domain as well as in frequency domain simulations. The goal of this research project is to build physics-based, guaranteed passive rational (pole/zero) broadband circuit models, starting from existing approximated models, whose passivity is not guaranteed.

Researcher(s)

• Promoter: Dhaene Tom
• Co-promoter: Broeckhove Jan
• Co-promoter: Cuyt Annie
• Co-promoter: Verdonk Brigitte

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project aims at: 1. designing and building POCs of an IP based storage environment, capable of storing, securing and accessing the data. The research will cover research in the area of fibre channel and iSCSI based SANs and disk connection networks, different types and quality of storage, as well as GPFS (general parallel file system) and grid computing. 2. designing and building POCs of a retrieval mechanism, besed on relevant metadata information, stored along with the actual data. The metadata model and the retrieval of the data based on search engines and content management is part of the project. 3. constantly monitoring the economical feasibility of the designed solutions, based on the business cases encountered in the market today. This means that market needs are monitored and mapped onto the actual research been carried out in this project.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project website

Project type(s)

• Research Project

Abstract

The overall objective of the CHAMP project is to develop a methodology and dimensioning rules to ensure that multiple applications (HSI, VoIP, SDTV, HDTV, gaming, multimedia-rich applications) can run at high enough quality over access and home networks.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main purpose of this project is to set up several new queueing models as well as developing efficient computational methods that allow us to determine the most significant performance measures (for instance, the waiting time distribution or the number of users that can share a resource without violating the agreement between the end users and the network provider). The choice of these models is driven by the latest developments in the telecommunication field. For a limited number of models we already obtained some preliminary results during the current FWO postdoc fellowship. The emphasis of the ongoing project lies mostly on applying existing models to broadband access networks, while the development of new models is of lesser importance. It is thus fair to state that the scope of this project proposal is somewhat more general.

Researcher(s)

• Promoter: Blondia Chris
• Fellow: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Different existing routing algorithms will be compared with special attention for throughput scalability and the speed of the setup of new connections. This will happen with the focus on the application of ad hoc networks in disaster scenarios. Apart from the comparison of existing routing algorithms research will be done on new routing algorithms that not only enhance the throughput, scalability and speed of connection setup, but can also guarantee quality of service for e.g. videostreams.

Researcher(s)

• Promoter: Blondia Chris
• Fellow: Van de Velde Erwin

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project fits in an initiative by the Flemish Government aimed at developing a pilot Grid infrastructure in Flanders. This infrastructure must allow Flemish researchers to gain experience working with Grid technologies, to cooperate in European Grid projects and gain access to the compute and storage capacity of international grid systems. Grid technologies create an enormous potential. They provide coordinated and integrated access to resources that are distributed on a wide area scale and are managed in a large numbers of administrative domains. Grid technologies will give rise to completely new approaches of scientific cooperation over the Internet, sometimes referred to as e-Science, based on virtual organizations that stand for worldwide collaborations. In this way, grid technologies will make it possible to tackle larger and more complex scientific problems.

Researcher(s)

• Promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The purpose of WIDENS is to design, prototype and validate a vertically integrated rapidly deployable and scalable communication system for future public safety, emergency and disaster applications. The project focuses on designing a single hot spot, which can be easily deployed, optimised for high bitrate throughput (over 2Mbit/s) and interoperable with existing core networks and present private mobile radio systems (such as TETRA and Tetrapol).The system concept is based on ad hoc network technologies.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The project is focusing on the development of novel and generic technologies for the support of the mobile multimedia network of the future. The network should support the mobility (interdomain, intradomain and within an access network) and should provide end-to-end QoS with different characteristics (depending on the service, terminal, access and wireless link technology) and in a point-to-point or point-to-multipoint mode (multicast).

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

During the design process of a complex physical system, computer-based simulations are often used to limit the number of expensive prototypes. However, despite the steady and continuing growth of computing power and speed, the computational cost of complex high-accuracy simulations (such as EM simulations) can also be high, especially during optimization and sensitivity analysis, since a single simulation of a design may take several minutes or even hours to complete. Our research will focus on adaptive physics-based system identification and meta-modeling techniques, in one or more dimensions of the design space, using sparse data. The data points in the design space will be carefully selected, and their number will be limited as much as possible, by exploring the design space. Meta-models are fast running surrogate approximations of complex time-consuming computer simulations. A meta-model is a compact "model of a model", and can be used for design optimization and design space visualization. It allows the designer to carry out real time evaluations of a design.

Researcher(s)

• Promoter: Dhaene Tom

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The purpose of WIDENS is to design, prototype and validate a vertically integrated rapidly deployable and scalable communication system for future public safety, emergency and disaster applications. The project focuses on designing a single hot spot, which can be easily deployed, optimised for high bitrate throughput (over 2Mbit/s) and interoperable with existing core networks and present private mobile radio systems (such as TETRA and Tetrapol).The system concept is based on ad hoc network technologies. The objective of the EURO-NGI is to integrate the European research on the design and dimensioning of the future Third Generation Internet. The main topics addressed by the NoE are: Mastering the technology diversity (vertical and horizontal integration) for the design of efficient and flexible 3Gi architectures. Providing required innovative traffic engineering architectures adapted to the new requirements and developing the corresponding appropriate quantitative methods.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The objective of the NoE is to integrate the European research on the design and dimensioning of the future Third Generation Internet. The main topics addressed by the NoE are: Mastering the technology diversity (vertical and horizontal integration) for the design of efficient and flexible 3Gi architectures. Providing required innovative traffic engineering architectures adapted to the new requirements and developing the corresponding appropriate quantitative methods.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The main goal of this project is the development of a fully automated modeling and identification technique to build compact meta-models for linear systems, based on a limited number of adaptively selected data samples in the design space of interest. Meta-models are fast running surrogate approximations of complex computer simulations. A meta-model can be used for design optimization and design space visualization.

Researcher(s)

• Promoter: Dhaene Tom

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Broeckhove Jan
• Fellow: Sytcheva Arina

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The aim of the project is to design a framework of protocols and procedures and to analyze its ability to provide end-to-end QoS provisioning in a network with heterogeneous access networks for highly mobile users. The project consists of the following parts: -Protocols for QoS Support in Mobility Management Schemes -Impact of wired and wireless access topologies on mobility management -Testbed and Experiments

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Research aiming at the development of a distributed platform for high-performance calculations, to be executed on a multitude of available computers, is presented. The primary applications considered are computational solutions of quantum mechanical many-body problems. The best distribution paradigm will be selected for this type of applications, and typical calculations for three-cluster nuclear spectroscopic quantities will be considered as a proof of concept.

Researcher(s)

• Promoter: Arickx Frans

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

A communications protocol can be described adequately, albeit informally, in several ways. But the most precise method is a formal model such as a finate state machine. The advantage of using a formal model is that one can proof the soundness and completeness of the proposed protocol. The goal of this project is the development of a formal model of the JRM (Java Reliable Multipeer) protocol. This will be accomplished using a time-driven finate state machine.

Researcher(s)

• Promoter: Dhaene Tom
• Co-promoter: Stuer Gunther

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

One of the key technological challenges to advance our knowledge-based society is the development of mobile multimedia communication systems and networks. Some general trends we observe today are : the increasing importance of multimedia services, the unprecedented growth of mobile telephony, the increasing importance of the Internet protocol as the integrating network technology, the importance of wireless access to the network, the increasing diversity of terminals. It is interesting to observe that the push towards wireless networks, the diversity in terminals and services, and the requirement for QoS and mobility, results in a situation where services should be matched to physical layer aspects (and of course with the networks and systems in between). The IAP/PAI consortium has bundled the expertise at the different levels required for the development of future mobile multimedia communication systems and networks. As a result, the project has been structured in a natural top-down approach, starting from the applications and ending with physical layer aspects. The interaction between these different fields of expertise will form a strong basis for the success of the project. The project is structured as follows : WPl : Applications WP2 : Network Design WP3 : Traffic and Performance Modelling WP4 : Modulation, Channel Coding and Propagation The major goals of the project can be summarized as follows : .Development of a software architecture for the use of dynamic metadata for the support of multimedia applications in a mobile environment. .The development ofhighly-scalable, object-based video coding schemes. .To develop new terminal concepts taking into account the format in which the data are transported between the multimedia source and a portable terminal. .To design and evaluate new protocols that allow seamless hand-offs and support end-to-end QoS for mobile terminals/services in homogeneous and heterogeneous network environments (including multicast support and the use of active networking). .To study generic queueing models which deal with connection and handover blocking probability in a wireless environment. To develop a generic stochastic model for the evaluation of macro- and micro- mobility solutions on the network layer . .To develop next generation QoS-enabled mobile ad hoc networks supporting heterogeneous and fast moving terminals based on active networking technology .To develop advanced routing algorithms and model contention resolution and reservation schemes. .To model web-type applications and to develop traffic models including characteristics related to mobility . .To study the performance of appropriate error detection and retransmission techniques (ARQ protocols) in a mobile and wireless environment (including TCP enhancements). .Design of receiver algorithms (synchronization, equalization, detection) for advanced modulation and transmission techniques- .The exploitation of multiple transmit and receive antennas and so-called smart antenna principles to enhance capacity and performance, and applying space-time coding to such multiple-input multiple- output systems- .The development of iterative (turbo) processing in multiuser detection, interference suppression, parameter estimation and error correction. Design of advanced compact antennas and free space electromagnetic wave propagation models for high bandwidth short distance wireless communications.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Arickx Frans
• Co-promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The goal is to develop efficient univariate and multivariate sampling algorithms, based on rational and polynomial interpolation, which establish accurate surrogate models for linear dynamic systems, such as passive microwave and RF systems. This reflective exploration technique must sample the (one- or multidimensional) parameter space in an optimal way in order to minimize the number of samples, without assuming any a priori knowledge of the system.

Researcher(s)

• Promoter: Dhaene Tom
• Co-promoter: Cuyt Annie
• Co-promoter: Verdonk Brigitte

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Dhaene Tom

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The project will study queueing models with variable service capacity, with the emphasis on fundamental, conceptual, generic aspects. These models have applications in the area of telecommunications, more specifically for the performance evaluation of the buffers that are used in various network elements for the temporary storage of information units.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The project goal is to contribute to the development of next generation transport networks using optical technologies. The focus will be on the networking aspects. It is envisaged that IP will be the convergence layer of future multimedia traffic, including the classical IP based data traffic and the classical telephony traffic. A clear understanding of optical circuit switched networks will be obtained. Major results will be obtained in the field op control architecture and routing strategies (for different network architectures). Resilience strategies will be developed and evaluated in great detail. A better understanding of optical packet switched networks will be obtained. Major emphasis will be on the node architectures. The partners are confident that it will be possible to analyse the specific queueing models necessary to evaluate the performance of optical packet switched networks by extending the techniques already developed in the past for other types of communication networks. Also a comparison between packet and circuit switched optical networks will be performed. A planning approach will be developed to support operators and vendors in strategic decisions concerning future network architectural options.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Researcher(s)

• Promoter: Broeckhove Jan

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

The objective of this project is to evaluate the performance of random access algorithms. Such algorithms are deployed in the current and next generation access networks, e.g., HFC networks, to allow for end users to reserve uplink bandwidth. Both a theoretical as well as a practical approach is used. Moreover, we also aim at further developing the theoretical tools, such as queuing theory and stochastic processes, often used within such a context.

Researcher(s)

• Promoter: Blondia Chris
• Fellow: Van Houdt Benny

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

This project will study the architectural and protocol-related issues of content delivery technologies, as well as the problems associated with the performance and resource planning in CDNs. With respect to the latter studies, a lot of attention will be devoted specifically to (audio and) video services, which are considered to be the driving forces behind this CDN boom.

Researcher(s)

• Promoter: Blondia Chris

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project

Abstract

Starting from observed data (like traffic on the Internet), robust statistical methods (i.e., techniques that give reliable results even when deviations occur in the input data) will be applied to construct a model for the observed system. From the specific architecture and structure of the system one can often derive interesting properties of the performance measure in advance, such as its monotonicity relative to a given system parameter, or its asymptotic behavior. These properties are helpful when constructing the performance measure, but by themselves they are not sufficient. Robust, efficient and accurate approximations of the exact solution are indispensable. A possible approach is based on power series, but since many performance functions have singularities a better approach is to use Pade approximations.

Researcher(s)

• Promoter: Blondia Chris
• Co-promoter: Cuyt Annie
• Co-promoter: Rousseeuw Peter

Research team(s)

• Internet Data Lab (IDLab)

Project type(s)

• Research Project