Research Peter Hellinckx

Abstract

Buildings contribute to 40% of global energy consumption, with 36% attributed to heating, ventilation, and air-conditioning (HVAC). Therefore, optimizing HVAC control in buildings is crucial in the transition to a more sustainable society. Model predictive control (MPC) and (deep) reinforcement learning (DRL) have been explored for optimal control strategies, producing promising results. However, their performance depends on the underlying simulation model's accuracy, which is why an accurate model throughout the building's lifecycle is important. Physics-based models introduce discrepancies due to necessary simplifications, called the sim-to-real-gap. Closing this gap requires expert knowledge to increase the model's complexity, which is often not feasible. Given the emergence of smart, sensor-equipped buildings, data-driven solutions are possible, enabling a hybrid model that exploits the advantages of data-driven and physics-based models. First, data-driven models, like for example deep neural networks (DNNs), are added to the physics-based model on the level of the components to close the sim-to-real-gap. Second, as components degrade, the sim-to-real-gap will grow again and is closed using the same approach. Third, the hybrid model facilitates automatic fault detection and diagnosis (AFDD) using results from the adjustment process. Finally, an assessment of energy loss due to component degradation guides cost-optimal maintenance strategies.

Researcher(s)

• Promoter: Hellinckx Peter
• Co-promoter: Verhaert Ivan
• Fellow: Houben Pieter Jan

Research team(s)

• Modelling for Sustainability (M4S)

Project type(s)

• Research Project

Abstract

Collective heating and cooling in apartment buildings or district heating simplifies the sustainability of heat generation. It facilitates smart electrification and an efficient shift to renewable heat sources. However, faults in the network or more generally suboptimal design, control and behavior of the heat distribution lead to efficiency losses from 10% to more than 50%. The growth of available data and progress in the field of data science opens up the opportunity to research and develop fault detection (FD) and fault detection + diagnosis (FDD) algorithms and techniques. However, the diversity in buildings and heating concepts limits their potential and functionality if they are based solely on measurement data. In FDD-accelerator an emulator will be built that is able to generate high quality labelled data on faults and suboptimal behaviour and able to validate potential FD(D) solutions. In the emulator methodologically structured expert knowledge is translated into a modular framework (digital twin) able to simulate suboptimal behaviour. The framework is combined with a lab set-up in which faults can be mimicked for validation, providing already some insight into the sim-to-real gap, which can be later exploited in further research.

Researcher(s)

• Promoter: Verhaert Ivan
• Co-promoter: Hellinckx Peter

Research team(s)

• Energy and Materials in Infrastructure and Buildings

Project type(s)

• Research Project

Abstract

In the worldwide R&D on computer-assisted and autonomous navigation the DDSHIP project will contribute by setting a new process flow methodology and test platform for validation and certification through investigations on: • more accurate and robust perception and situational awareness of the waterborne world around the ship in dense traffic and harsh weather conditions; • the accurate representation of the real behaviour of the ship in complex waterways with low under keel clearances and nearby banks and infrastructure; • the safe and smooth control of the ship through model predictive AI-trained controllers providing necessary collision avoidance. As accidents on waterways are mainly attributed to human actions in combination with failures of technical hard- and software or environmental circumstances, the support of captains, pilots or skippers on board the manned ship or the operator from a remote operation centre on an unmanned ship, this research should prove the capabilities of existing technologies (camera, sensors, manoeuvring model prediction, path-planning and steering) leading to smarter - more accurate and higher reliability – control.

Researcher(s)

• Promoter: Vanlanduit Steve
• Co-promoter: Hellinckx Peter

Research team(s)

• Industrial Vision Lab (InViLab)

Project type(s)

• Research Project

Abstract

The Flemish infrastructure sector faces a major challenge to implement new technologies such as IoT sensors, AI, VR, XR, digital 3D modelling packages, BIM software, 3D scanning technology, drones and business models based on internal and external, whether or not real-time not real-time data flow, to be implemented. Digital Information Management (DIM) in Infrastructure (DIMInfra) is an information management model that allows, through improved internal and external information flows, to optimise business processes and thus achieve efficiency gains. In addition, DIM opens the way to new business opportunities for companies throughout the value chain. Central in the ecosystem are the contractors class 4 to 8. They form the bridge between, on the one hand, the target group 'Execution' target group (Surveyors, Subcontractors and suppliers, including transport firms and software suppliers) and on the other hand the 'Construction Partner' target group (Building owners, including contracting cities/municipalities and Flemish infrastructure managers, Consultancy companies, Control and certification organisations). Concrete goals of the project are: Encourage companies to implement the DIM model through company-specific actions by providing insight and practical demonstrations of DIM and renewed business models, thereby increasing efficiency and reducing costs. With DIMInfra, companies gain insight through a technology matrix by subgroup into emerging and promising technologies that support DIM as well as the application of these technologies within the context of the infrastructure sector. This accelerates the sector's digital transition. A final goal is to provide tools through roadmaps and digital learning modules that facilitate the implementation of DIM, in order to accelerate the roll-out of DIM in the field. The project is a collaboration between University of Antwerp and PXL Hogeschool, supported by more than 35 partners from the infrastructure sector.

Researcher(s)

• Promoter: Van den bergh Wim
• Co-promoter: Hellinckx Peter

Research team(s)

• Sustainable Pavements and Asphalt Research (SuPAR)

Project type(s)

• Research Project

Abstract

From noise-sensitive areas to oases of silence: with the large-scale citizen science project De Oorzaak, De Morgen (DM), the University Hospital Antwerp (UZA) and UAntwerpen are focusing on noise and noise perception in an urban environment. In 2024-2025, we will investigate how residents of different neighborhoods in Antwerp, Ghent and Leuven appreciate the environmental noise present. By means of questionnaires (subjective), smart sound sensors (objective) and medical research (UAntwerpen and UZA) we will classify which sounds are heard, what sound level these sounds have, how these sounds are experienced and what impact they have on health, stress, sleep and quality of life.

Researcher(s)

• Promoter: Vuye Cedric
• Co-promoter: Casas Ruiz Lidia
• Co-promoter: Couscheir Karolien
• Co-promoter: de Bruijn Gert-Jan
• Co-promoter: Dens Nathalie
• Co-promoter: Hellinckx Peter
• Co-promoter: Lembrechts Jonas
• Co-promoter: Poels Karolien
• Co-promoter: Spacova Irina
• Co-promoter: Van Hal Guido
• Co-promoter: Vanoutrive Thomas

Research team(s)

• Sustainable Pavements and Asphalt Research (SuPAR)

Project website

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

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)

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

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

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

District Heating and Cooling (DHC) is a promising technology to shift to a sustainable energy supply, offering flexibility to the electric grid. Thermal storage can provide the necessary flexibility to balance production an demand for both electrical and thermal renewable energy sources (RES). Especially, the integration of decentralised thermal prosumers (e.g. boosters, thermal solar panel) in DHC have great potentials to improve the overall efficiency. Therefore, future DHC will need advanced control strategies facilitating the operation of prosumers-based DHC and providing flexibility to RES-dominated electric grids. Hereby, two main questions arise: (i) how should the temperature be controlled to improve the energetic, ecologic and economic performance of a DHC? And (ii) how to take into account the requirements of every direct stakeholder in the DHC? By simulating the DHC's behaviour, considering hydronics and prosumer behaviour, I will research the potential of a data-based control strategy, including multi-agent reinforcement learning (MARL). Every agent (per consumer, heat storage, etc.) pursues the local as well as the global objectives. The RL-agents are capable of self-learning a control strategy based on feedback (rewards). Besides valorisation throughout implementing such controls, the feedback and/or reward itself can be subject of follow-up research with respect to policy support.

Researcher(s)

• Promoter: Verhaert Ivan
• Co-promoter: Hellinckx Peter
• Fellow: Jacobs Stef

Research team(s)

• Energy and Materials in Infrastructure and Buildings

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)

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

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

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

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
• Promoter: Weyn Maarten
• Co-promoter: Famaey Jeroen
• Co-promoter: Hellinckx Peter
• Co-promoter: Latré Steven
• Co-promoter: Mannens Erik
• 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

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

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)

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

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

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

The objective of this project is to demonstrate how patient-related physiotherapy data that is collected and stored in a structured way, can be used for data-analysis by the use of deep learning, a part of a broader family of machine learning methods. Neural networks will be used to determine which therapeutic approach can be best used for what type of patient to increase physical activity, thereby demonstrating the feasibility of using data-analysis to develop effective therapeutic strategies in patients with cardiorespiratory and metabolic diseases. Demonstrating the feasibility of data gathering, storage and analysis in physiotherapy in internal diseases in a primary care setting will be a first and major step in developing data-driven therapy. The results of this project will facilitate and enable further research in the development of data-driven medicine in multiple diseases, as well as the development of data-monitoring and tele-coaching application in healthcare. The combined expertise of both research groups, in partnership with the Belgian Physiotherapy Association (Axxon), allows this consortium to take a head start in data-driven physiotherapy research and to become a pioneer in this field in Europe.

Researcher(s)

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

Research team(s)

• Movement Antwerp (MOVANT)

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

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

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

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)

• Modelling for Sustainability (M4S)
• 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

Energy efficiency in the built environment plays a key role in the transition towards a sustainable zero-carbon future. More specifically, renewables and industrial waste heat should be integrated in today's energy distribution systems. This integration is facilitated by so-called thermal grids, i.e. large systems at building or district level that consist of heat (and/or cold) sources and sinks, which are all connected by distribution pipes. The operation of thermal grids has highly complex dynamics because of two reasons. First, -analogue to the electrical grid- the intermittent loads of the sources and sinks should be aligned to ensure thermal and sanitary comfort of the end-users. Second, each type of thermal load (space heating, cooling and domestic hot water) requires a different temperature level. These temperature levels strictly affect both distribution losses and production efficiency. Currently, thermal grids are operated with static and mostly linear rule-based fuzzy logic control structures. Because of the simplicity and compactness of the linguistic approach of these types of controllers, trajectory following problems (such as heating and cooling reference tracking etc.) can successfully be accomplished. Yet, even though these solutions perfectly fit specific industrial applications, they do not offer any contribution to energy saving for complex thermal grids. Thus, the potential of thermal grids cannot be fully exploited by using conventional approaches. Indeed, primitive rule-based perspectives cannot fully optimize the alignment between production and demand, or the temperature set points along the grids. To sum up, they are designed for reliability, not for optimal efficiency. Optimizing controller dynamics of complex systems has been tackled in numerous areas of industrial applications: automotive, avionics, process industry etc. However, all these subsystems have mostly time-invariant dynamics and considerably less uncertainties that have serious effects on the aimed goal. This means that the data-driven algorithm can stop pre-processing input-output data after proposing an optimal solution under strict assumptions and constraints. On the other hand, environments such as thermal grids, which include high non-linearity, high complexity and time-varying parameters, require novel trends towards data-driven control methodologies. Despite their advantages and maturity, data-driven approaches have not adapted and penetrated into thermal grid applications (or HVAC systems in general). The reason is a lack of existing frameworks for implementation of these approaches and insufficient joint forces of HVAC and AI multi-disciplinary expertise. With this project, ID-Lab and EMIB, aim to set up a strong collaboration and obtain a leading role in the research of data-driven methodologies for optimizing energy efficiency in the building sector.

Researcher(s)

• Promoter: Verhaert Ivan
• Co-promoter: Hellinckx Peter
• Co-promoter: Van Riet Freek

Research team(s)

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: Hellinckx Peter

Research team(s)

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

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)

• Modelling for Sustainability (M4S)
• 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

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)

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

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

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

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

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: Hellinckx Peter

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

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

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

A market study of ICT solutions used in contemporary transport operations conducted within the PhD study of Valentin Carlan at the University of Antwerp shows a high heterogeneity of systems, data sources, applications and working practices. This mix leads to segregation of the logistics supply chain and hinders further optimization possibilities. In this context, there is no coordination or standardisation that ensures a straightforward approach to develop and implement cross-sector IT solutions. Additionally, based on interviews and shared experiences, gaps in current business processes are identified. Different integrative state-of-the-art solutions are currently in place, which are non-optimal, not flexible and expensive to maintain. These solutions force transport companies to lock themselves into their initial software solution, limiting any migration or integration opportunities with other third-party software. Within this, a universal middleware framework will be developed to achieve a more flexible and extended data integration of IT solutions among transport companies. The proposed framework consists of two core functions/layers, namely the data acquisition and presentation layer and the data processing and interactions layer. As a result, creating these chains of sub-systems allows designing analysis and interactions models which are able to autonomously trigger events. These chains are adaptable for each transportation company depending on their automation needs. For this , we will focus on developing two different systems in this layer, particularly an automatic safety warning information system (SWIS) and a planner's tool for fleet management (PTFM). The latter would incorporates two functionalities: a static planning tool (SPT) and a dynamic dispatching system (DDS).

Researcher(s)

• Promoter: Vanelslander Thierry
• Co-promoter: Hellinckx Peter

Research team(s)

• Transport and Regional Economics

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

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: De Meulenaere Paul
• Co-promoter: Hellinckx Peter

Research team(s)

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

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: 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: Hellinckx Peter

Research team(s)

• Internet Data Lab (IDLab)

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 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

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

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

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

The objective of this project is to develop and implement a robust IT architecture with a digital portal. This system allows the communication between all active parties of the asphalt sector during a construction work and stores data for later interventions. The architecture allows in each step and for each partner and device to communicate in real time in order to achieve an effective process. The project includes four demonstration cases.

Researcher(s)

• Promoter: Van den bergh Wim
• Co-promoter: Hellinckx Peter

Research team(s)

Project type(s)

• Research Project

Abstract

Multicore processors are increasingly used in mechatronic applications and need to endorse the realtime requirements of the related embedded software. In spite of their huge processing power, certain operational conditions may arise in which they show longer software execution times than reasonably expected. In this project, we will elaborate software timing analysis techniques which will lead to better configurations of multicore platforms with respect to the software execution time and more specifically to the unexpected outliers mentioned above. To this purpose, we will propose a modelling language that will allow for a formal description of the timing properties of real-time embedded multicore software. This modelling language will enable formal methods for schedulability analysis and design space exploration methods, such that timing outliers can be eliminated by suggesting alternative configurations for the multicore platform.

Researcher(s)

• Promoter: De Meulenaere Paul
• Co-promoter: Denil Joachim
• Co-promoter: Hellinckx Peter
• Fellow: Li Haoxuan

Research team(s)

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

Project type(s)

• Research Project

Abstract

This project represents a research contract awarded by the University of Antwerp. The supervisor provides the Antwerp University research mentioned in the title of the project under the conditions stipulated by the university.

Researcher(s)

• Promoter: Demeyer Serge
• Co-promoter: Blondia Chris
• Co-promoter: De Meulenaere Paul
• Co-promoter: Hellinckx Peter
• Co-promoter: Latré Steven
• Co-promoter: Peremans Herbert
• Co-promoter: Steckel Jan
• Co-promoter: Steenackers Gunther
• Co-promoter: Vangheluwe Hans
• Co-promoter: Vanlanduit Steve
• Co-promoter: Weyn Maarten
• Fellow: De Mey Fons
• Fellow: Hristoskova Anna

Research team(s)

• Antwerp Systems and software Modelling (AnSyMo)

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 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