CITI seminar – Lucien Etienne (IMT Lille-Douai) – 17/12 at 14:00

Title: Self trigger co-design using LASSO regression

Date and Place: 17th December 2020 14:00 – link

Speaker: Lucien Etienne (IMT Lille-Douai)

 

Abstract: 

Networked systems have become more and more pervasive in many modern industrial application. A good justification for their deployment is that they can be cheaper/faster to set in place as well are scalable while also enabling lower maintenance cost. In the past decade a new paradigm has been developed where the controller is not sampled periodically (i.e. with a time–triggered policy), but rather sampled when some condition has been met (Usually a stability or performance criterion being violated). After recalling some general element on classical control scheme ( Linear Quadratic regulator and model predictive control) In this talk, the control of a linear time invariant system with self triggered sampling is considered .
In order to address the controller computation and the future sampling schedule a sparse optimization problem will be considered. A relaxation of the optimal self triggered control can be formulated as a LASSO regression. Using the properties of the solution of the Lasso regression it is shown how to obtain a controller ensuring practical or asymptotic stability while reducing sampling of the control action.

 

Biography:

Dr. Lucien Etienne received a M.Sc. Degree in applied mathematics at the INSA Rouen in 2012 and a joint Ph.D. in automatic control from the university of L’aquila and the university of Cergy-Pontoise in 2016. From 2016 to 2017 he was a post doctoral researcher at Inria Lille-Nord Europe. Since 2017 He is an associate professor at Institut Mines-Télécom Lille Douai. His research interests include switched and hybrid systems, observer synthesis and sampled data systems.

 


PhD Defence: “Impulsive and Dependent Interference in IoT Networks”, Ce Zheng, 8th of December 2020 at 14:00PM

The defense will be streamed live here: link

 

Title

Impulsive and Dependent Interference in IoT Networks

Abstract

The number of devices in wireless IoT networks is now rapidly increasing and is expected to continue growing in the coming years.
To support this massive connectivity, a number of new technologies, collectively known as LPWAN, have been developed. Many devices in LPWANs limit their transmissions by duty cycle constraints; i.e., the proportion of time allocated for transmission. For nearby wireless networks using the same time-frequency resources, the increasing number of devices leads to a high level of unintended signals, known as interference.

In this thesis, we characterize the statistics of interference arising from LPWANs, with a focus on protocols related to NB-IoT and emerging approaches such as SCMA. Such a characterization is critical to improve signal processing at the receiver in order to mitigate the interference. We approach the characterization of the interference statistics by exploiting a mathematical model of device locations, signal attenuation, and the access protocols of individual interfering devices. While there has been recent work developing empirical models for the interference statistics, this has been limited to studies of the interference power, which has limited utility in receiver design. The approach adopted in this thesis has the dual benefits of providing a model for the amplitude and phase statistics and while also yielding insights into the impact of key network parameters. The first contribution in this work is to revisit interference in a single subcarrier system, which is widely used in current implementations of IoT networks. A basic model in this scenario distributes interfering devices according to a homogeneous Poisson point process. It has been long known that the resulting interference is well approximated via an alpha-stable model, rather than a Gaussian model. In this work, the \alpha-stable model is shown via theoretical and simulation results to be valid in a wider range of models, including the presence of guard zones, finite network radii, and non-Poisson point processes governing device locations. The second contribution in this thesis is the study, for the first time, of interference statistics in multi-carrier IoT networks, including those that exploit NB-IoT and SCMA. Motivated by the results in the single subcarrier setting, a multivariate model based on alpha-stable marginals and copula theory is developed. This model is verified by extensive simulations and further justified via a new, near-optimal, parameter estimation algorithm, which has very low complexity. The third part of this thesis applies the characterizations of the interference statistics to receiver design. A new design for nonlinear receivers is proposed that can significantly outperform the state-of-the-art in multi-carrier IoT systems. When receivers are restricted to be linear, the optimal structure is identified and the bit error rate characterized. Numerical results also illustrate how the average quantity of data interfering devices are required to transmit affects the receiver performance.

 

Jury

  • Prof. Claude Oestges (Ecole Polytechnique de Louvain, Belgium)
  • Assis. Prof. Lina Mroueh (Institut Supérieur d’Electronique de Paris, France)
  • Assoc. Prof. Mylene Pischella (Conservatoire National des Arts et Métiers, France)
  • Prof. Jean-François Hélard (INSA Rennes)
  • Assoc. Prof. Troels Pedersen (Univ. Aalborg, Denmark)
  • Prof. Gareth Peters (Heriot-Watt, UK)

CITI seminar – Ignacio Rodriguez (Aalborg University) – 10/12 at 14:00

Title: Experimental Research on Wireless Systems for Industrial Automation

Date and Place: 10th December 2020 14:00 – link to come

Speaker: Ignacio Rodriguez (Aalborg University)

Host: Maracas

 

Abstract: 

The fourth industrial revolution – or Industry 4.0 (I4.0), will introduce major shifts in the way that products will be manufactured in the future. By integrating different cyberphysical systems (CPS), Internet-of-Things (IoT) technologies and cloud computing; the factories of the future will be equipped with highly flexible manufacturing equipment offering also a high reliability, thereby increasing the overall production throughput. One of the key enablers for such revolution is wireless communication. By replacing existing wirelines in the current industrial equipment with wireless technologies, the overall cost of deployment will be reduced, while at the same time a faster re-configuration of the smart production facilities will be enabled. Moreover, the use of wireless technologies will also allow for new industrial use cases requiring full mobility support such as autonomous robots moving items over different workstations in the factory for the sake of manufacturing customized products. During this talk, the AAU Industrial Automation Applied Research Flow will be introduced and illustrated with application examples detailing the different steps from understanding the needs of a factory and the specific communication requirements of industrial use cases; to the final deployment and optimization of the wireless solutions.

 

Biography:

Ignacio Rodriguez received the B.Sc. and M.Sc. degrees in Telecommunication Engineering from University of Oviedo, Spain, and the M.Sc. degree in Mobile Communications and the Ph.D. degree in Wireless Communications from Aalborg University, Denmark. Since December 2016, he has been a Postdoctoral Researcher at the same institution, where he is currently coordinating the Industry 4.0 experimental research activities at the Wireless Communication Networks Section and the AAU 5G Smart Production Lab in collaboration with the Department of Materials and Production. Ignacio is also an External Research Engineer with Nokia Bell Labs, where he is involved in 3GPP and ITU-R standardization activities. His research interests are mainly related to radio propagation, channel modeling, radio network planning and optimization, machine-to-machine communications, ultra-reliable and low-latency communications, 5G and Industrial IoT. He is a co-recipient of the IEEE VTS 2017 Neal Shepherd Memorial Best Propagation Paper Award, and in 2019, he was awarded with the 5G-prize by the Danish Energy Agency and the Danish Society of Telecommunication Engineers.

 


CITI seminar – Howard Yang (Zhejiang University, China) – 26/11 at 14:00

Title: Spatiotemporal Modeling of Wireless Networks

Date and Place: 26 / 11 / 2020 14:00 – https://bbb.tuxlab.net/b/mal-ngv-xm6-qgd

Speaker: Howard Yang (Zhejiang University, China)

Host: Maracas

 

Abstract: 

The rapid growth of wireless applications has brought along new challenges for the next generation network, which is expected to manage a massive number of devices in real-time under a highly dynamic environment. To give an adequate response, it is of necessity to develop an analytical model with which designers can build intuitions, grasp insights, and identify critical issues. In this talk, I will describe a framework for the analysis of large-scale wireless networks in which the transceivers interact with each other through the interference they caused and hence are correlated in both space and time. The analysis straddles stochastic geometry and queueing theory to cope with the issues of spatially interacting queues, and arrive at handy expressions for the SINR distribution. As a result, a wide variety of systems/architecture can be devised based on this theoretical foundation. Specifically, I will demonstrate how to adopt such a mathematical model to the analysis of two particular network metrics, i.e., the packet delay and age of information, and the subsequent network deployment guidelines based on the analytical results.

 

Biography:

Howard Hao Yang received the B.Sc. degree in Communication Engineering from Harbin Institute of Technology (HIT), China, in 2012, and the M.Sc. degree in Electronic Engineering from Hong Kong University of Science and Technology (HKUST), Hong Kong, in 2013. He earned the Ph.D. degree in Electrical Engineering from Singapore University of Technology and Design (SUTD), Singapore, in 2017, and stayed three years as a postdoc. He is now an assistant professor with the ZJU-UIUC Institute, Zhejiang University. Dr. Yang’s background also features appointments at the Princeton University in 2018 – 2019, and the University of Texas at Austin in 2015 – 2016. His research interests cover various aspects of wireless communications, networking, and signal processing, currently focusing on the modeling of next-generation wireless networks, age of information, and federated learning.

 


PhD Defence: “Étalonnage in situ de l’instrumentation bas coût pour la mesure de grandeurs ambiantes : méthode d’évaluation des algorithmes et diagnostic des dérives”, Florentin Delaine, 4th of December 2020 at 10:30AM

The defense will be streamed live here: link

 

Title

Étalonnage in situ de l’instrumentation bas coût pour la mesure de grandeurs ambiantes : méthode d’évaluation des algorithmes et diagnostic des dérives

Abstract

In various fields going from agriculture to public health, ambient quantities have to be monitored in indoors or outdoors areas. For example, temperature, air pollutants, water pollutants, noise and so on have to be tracked. To better understand these various phenomena, an increase of the density of measuring instruments is currently necessary. For instance, this would help to analyse the effective exposure of people to nuisances such as air pollutants. The massive deployment of sensors in the environment is made possible by the decreasing costs of measuring systems, mainly using sensitive elements based on micro or nano technologies. The drawback of this type of instrumentation is a low quality of measurement, consequently lowering the confidence in produced data and/or a drastic increase of the instrumentation costs due to necessary recalibration procedures or periodical replacement of sensors. There are multiple algorithms in the literature offering the possibility to perform the calibration of measuring instruments while leaving them deployed in the field, called in situ calibration techniques.

The objective of this thesis is to contribute to the research effort on the improvement of data quality for low-cost measuring instruments through their in situ calibration. In particular, we aim at 1) facilitating the identification of existing in situ calibration strategies applicable to a sensor network depending on its properties and the characteristics of its instruments; 2) helping to choose the most suitable algorithm depending on the sensor network and its context of deployment; 3) improving the efficiency of in situ calibration strategies through the diagnosis of instruments that have drifted in a sensor network. Three main contributions are made in this work. First, a unified terminology is proposed to classify the existing works on in situ calibration. The review carried out based on this taxonomy showed there are numerous contributions on the subject, covering a wide variety of cases. Nevertheless, the classification of the existing works in terms of performances was difficult as there is no reference case study for the evaluation of these algorithms. Therefore in a second step, a framework for the simulation of sensors networks is introduced. It is aimed at evaluating in situ calibration algorithms. A detailed case study is provided across the evaluation of in situ calibration algorithms for blind static sensor networks. An analysis of the influence of the parameters and of the metrics used to derive the results is also carried out. As the results are case specific, and as most of the algorithms recalibrate instruments without evaluating first if they actually need it, an identification tool enabling to determine the instruments that are actually faulty in terms of drift would be valuable. Consequently, the third contribution of this thesis is a diagnosis algorithm targeting drift faults in sensor networks without making any assumption on the kind of sensor network at stake. Based on the concept of rendez-vous, the algorithm allows to identify faulty instruments as long as one instrument at least can be assumed as non-faulty in the sensor network. Across the investigation of the results of a case study, we propose several means to reduce false results and guidelines to adjust the parameters of the algorithm. Finally, we show that the proposed diagnosis approach, combined with a simple calibration technique, enables to improve the quality of the measurement results. Thus, the diagnosis algorithm opens new perspectives on in situ calibration.

 

Jury

  • M. Jean-Luc Bertrand-Krajewski, Professeur des Universités, Université de Lyon, INSA Lyon, DEEP (Rapporteur)
  • M. Romain Rouvoy, Professeur des Universités, Université de Lille, Spirals (Rapporteur)
  • Mme Nathalie Redon, Maître de conférences, IMT Lille Douai, SAGE (Examinatrice)
  • M. Gilles Roussel, Professeur des Universités, Université du Littoral Côte d’Opale, LISIC (Examinateur)
  • Mme Bérengère Lebental, Directrice de recherche, Institut Polytechnique de Paris, École Polytechnique, LPICM (Directrice de thèse)
  • M. Hervé Rivano, Univeristé de Lyon, INSA Lyon, CITI Lab (Co-directeur de thèse)
  • M. Éric Peirano, Directeur général adjoint en charge de la R&D, Efficacity (Invité)
  • M. Matthieu Puigt, Maître de conférences, Université du Littoral Côte d’Opale, LISIC (Invité)

PhD Defence: “Deep Multi-Agent Reinforcement Learning for Dynamic and Stochastic Vehicle Routing Problems”, Guillaume Bono, 28th of October 2020 at 2PM

The defense will take place in amphitheater Chappe and you are all welcome to attend as long as there are enough place (35 persons max).
It will be also streamed live on youtube at: https://youtu.be/fvCz5ZXYN_I

 

Title

Deep Multi-Agent Reinforcement Learning for Dynamic and Stochastic Vehicle Routing Problems

Abstract

Routing delivery vehicles in dynamic and uncertain environments like dense city centers is a challenging task, which requires robustness and flexibility. Such logistic problems are usually formalized as Dynamic and Stochastic Vehicle Routing Problems (DS-VRPs) with a variety of additional operational constraints, such as Capacitated vehicles or Time Windows (DS-CVRPTWs). Main heuristic approaches to dynamic and stochastic problems simply consist in restarting the optimization process on a frozen (static and deterministic) version of the problem given the new information. Instead, Reinforcement Learning (RL) offers models such as Markov Decision Processes (MDPs) which naturally describe the evolution of stochastic and dynamic systems. Their application to more complex problems has been facilitated by recent progresses in Deep Neural Networks, which can learn to represent a large class of functions in high dimensional spaces to approximate solutions with high performances. Finding a compact and sufficiently expressive state representation is the key challenge in applying RL to VRPs. Recent work exploring this novel approach demonstrated the capabilities of Attention Mechanisms to represent sets of customers and learn policies generalizing to different configurations of customers. However, all existing work using DNNs reframe the VRP as a single-vehicle problem and cannot provide online decision rules for a fleet of vehicles.
In this thesis, we study how to apply Deep RL methods to rich DS-VRPs as multi-agent systems. We first explore the class of policy-based approaches in Multi-Agent RL and Actor-Critic methods for Decentralized, Partially Observable MDPs in the Centralized Training for Decentralized Control (CTDC) paradigm. To address DS-VRPs, we then introduce a new sequential multi-agent model we call sMMDP. This fully observable model is designed to capture the fact that consequences of decisions can be predicted in isolation. Afterwards, we use it to model a rich DS-VRP and propose a new modular policy network to represent the state of the customers and the vehicles in this new model, called MARDAM. It provides online decision rules adapted to the information contained in the state and takes advantage of the structural properties of the model. Finally, we develop a set of artificial benchmarks to evaluate the flexibility, the robustness and the generalization capabilities of MARDAM. We report promising results in the dynamic and stochastic case, which demonstrate the capacity of MARDAM to address varying scenarios with no re-optimization, adapting to new customers and unexpected delays caused by stochastic travel times. We also implement an additional benchmark based on micro-traffic simulation to better capture the dynamics of a real city and its road infrastructures. We report preliminary results as a proof of concept that MARDAM can learn to represent different scenarios, handle varying traffic conditions, and customers configurations.

 

Jury

  • François Charpillet, Research Director at INRIA Nancy Grand Est, Reviewer
  • Romain Billot, Professor at IMT Atlantique, Reviewer
  • René Mandiau, Professor at Université Polytechnique des Hauts de France, Examiner
  • Aurélie Beynier, Associate Professor at Sorbonne Université, Examiner
  • Christian Wolf, Associate Professor at INSA de Lyon, Examiner
  • Olivier Simonin, Professeur à l’INSA de Lyon, Thesis director
  • Jilles Dibangoye, Associate Professor at INSA de Lyon, Co-supervisor
  • Laëtitia Matignon, Associate Professor at Université Lyon 1, Co-supervisor
  • Florian Pereyron, Research Engineer at Volvo Group, Co-supervisor

PhD Defence: “Enhancing Transparency and Consent in the IoT”, Victor Morel, 24th of September 2020 at 3PM

Title

Enhancing Transparency and Consent in the IoT

 

Abstract

In an increasingly connected world, the Internet permeates every aspect of our lives. The number of devices connected to the global network is rising, with prospects foreseeing 75 billions devices by 2025. The Internet of Things envisioned twenty years ago is now materializing at a fast pace, but this growth is not without consequence. The increasing number of devices raises the possibility of surveillance to a level never seen before. A major step has been taken in 2018 to safeguard privacy, with the introduction of the General Data Protection Regulation (GDPR) in the European Union. It imposes obligations to data controllers on the content of information about personal data collection and processing, and on the means of communication of this information to data subjects. This information is all the more important that it is required for consent, which is one of the legal grounds to process personal data. However, the Internet of Things can pose difficulties to implement lawful information communication and consent management. The tension between the requirements of the GDPR for information and consent and the Internet of Things cannot be easily solved. It is however possible. The goal of this thesis is to provide a solution for information communication and consent management in the Internet of Things from a technological point of view. To do so, we introduce a generic framework for information communication and consent management in the Internet of Things. This framework is composed of a protocol to communicate and negotiate privacy policies, requirements to present information and interact with data subjects, and requirements over the provability of consent. We support the feasibility of this generic framework with different options of implementation. The communication of information and consent through privacy policies can be implemented in two different manners: directly and indirectly. We then propose ways to implement the presentation of information and the provability of consent. A design space is also provided for systems designers, as a guide for choosing between the direct and the indirect implementations. Finally, we present fully functioning prototypes devised to demonstrate the feasibility of the framework’s implementations. We illustrate how the indirect implementation of the framework can be developed as a collaborative website named Map of Things. We then sketch the direct implementation combined with the agent presenting information to data subjects under the mobile application CoIoT.

 

 

Jury

  • Patricia Serrano Alvarado, Maître de conférences HDR à l’Université de Nantes, rapporteur
  • Gerardo Schneider, Professor at the University of Gothenburg, rapporteur
  • Félicien Vallet, Docteur ingénieur au sein du service de l’expertise technologique de la CNIL, examinateur
  • Hervé Rivano, Professeur des universités à l’Insa de Lyon, examinateur
  • Daniel Le Métayer, Directeur de recherche à Inria, Directeur de thèse
  • Claude Castelluccia, Directeur de recherche à Inria, co-Directeur de thèse

PhD Defence: “Privacy Challenges in Wireless Communications of the Internet of Things”, Guillaume Celosia, 22th of September 2020 at 9.30AM

Title

Privacy Challenges in Wireless Communications of the Internet of Things

 

Abstract

Also known as the Internet of Things (IoT), the proliferation of connected objects offers unprecedented opportunities to consumers. From fitness trackers to medical assistants, through smarthome appliances, the IoT objects are evolving in a plethora of application fields. However, the benefits that they can bring to our society increase along with their privacy implications. Continuously communicating valuable information via wireless links such as Bluetooth and Wi-Fi, those connected devices support their owners within their activities. Most of the time emitted on open channels, and sometimes in the absence of encryption, those information are then easily accessible to any passive attacker in range. In this thesis, we explore two major privacy concerns resulting from the expansion of the IoT and its wireless communications: physical tracking and inference of users information. Based on two large datasets composed of radio signals from Bluetooth/BLE devices, we first defeat existing anti-tracking features prior to detail several privacy invasive applications. Relying on passive and active attacks, we also demonstrate that broadcasted messages contain cleartext information ranging from the devices technical characteristics to personal data of the users such as e-mail addresses and phone numbers. In a second time, we design practical countermeasures to address the identified privacy issues. In this direction, we provide recommendations to manufacturers, and propose an approach to verify the absence of flaws in the implementation of their protocols. Finally, to further illustrate the investigated privacy threats, we implement two demonstrators. As a result, Venom introduces a visual and experimental physical tracking system, while Himiko proposes a human interface allowing to infer information on IoT devices and their owners.

 

Jury

  • Kasper Rasmussen – Associate Professor, University of Oxford – Rapporteur
  • Bernard Tourancheau – Professeur des Universités, Université Grenoble Alpes – Rapporteur
  • Sonia Ben Mokhtar – Directeur de Recherche, CNRS – Examinateur
  • Jean-Marie Gorce – Professeur des Universités, INSA Lyon – Examinateur
  • Vincent Nicomette – Professeur des Universités, INSA Toulouse – Examinateur
  • Valérie Viet Triem Tong – Professeur des Universités, CentraleSupélec Rennes – Examinateur
  • Daniel Le Métayer – Directeur de Recherche, Inria – Directeur de thèse
  • Mathieu Cunche – Maître de Conférences, INSA Lyon – co Directeur de thèse

PhD Defence: “Medium Access Control Layer for Dedicated IoT Networks”, Abderrahman Ben Khalifa, 30th of July 2020 at 2PM

The defense will be available at https://join.skype.com/dbnmuw06rBou

Title

Medium Access Control Layer for Dedicated IoT Networks

 

Abstract

Les réseaux dédiés pour l’Internet des Objets sont apparus avec la promesse de connecter des milliers de nœuds, voire plus, à une seule station de base dans une topologie en étoile. Cette nouvelle logique représente un changement fondamental dans la façon de penser les réseaux, après des décennies pendant lesquelles les travaux de recherche se sont focalisés sur les réseaux multi-sauts.
Les réseaux pour l’Internet des Objets se caractérisent par la longue portée des transmissions, la vaste couverture géographique, une faible consommation d’énergie et un bas coût de mise en place. Cela a rendu nécessaire des adaptations à tous les niveaux protocolaires afin de satisfaire les besoins de ces réseaux. Plusieurs acteurs sont en concurrence sur le marché de l’Internet des Objets, essayant chacun d’établir la solution la plus efficiente. Ces acteurs se sont concentrés sur la modification de la couche physique, soit au niveau de la partie matérielle, soit par la proposition de nouvelles techniques de modulation. Toutefois, en ce qui concerne la solution de contrôle d’accès au canal (connue sous le nom de couche MAC), toutes les solutions proposées par ces acteurs se fondent sur des approches classiques, tel que Aloha et CSMA.
L’objectif de cette thèse est de proposer une solution MAC dynamique pour les réseaux dédiés à l’Internet des Objets. La solution proposée a la capacité de s’adapter aux conditions du réseau. Cette solution est basée sur un algorithme d’apprentissage automatique, qui apprend de l’historique du réseau afin d’établir la relation entre les conditions du réseau, les paramètres de la couche MAC et les performances du réseau en termes de fiabilité et de consommation d’énergie. La solution possède également l’originalité de faire coexister des nœuds utilisant de différentes configurations MAC au sein du même réseau. Les résultats de simulations ont montré qu’une solution MAC basée sur l’apprentissage automatique pourrait tirer profit des avantages des différents protocoles MAC classiques. Les résultats montrent aussi qu’une solution MAC cognitive offre toujours le meilleur compromis entre fiabilité et consommation d’énergie, tout en prenant en compte l’équité entre les nœuds du réseau. La solution MAC cognitive testée pour des réseaux à haute densité a prouvé des bonnes propriétés de passage à l’échelle par rapport aux protocoles MACs classiques, ce qui constitue un autre atout important de notre solution.

 

Jury

  • M. Antoine GALLAIS, Professeur Université Polytechnique Hauts de France, Rapporteur
  • M. Congduc PHAM, Professeur Université de Pau et des Pays de l’Adour, Rapporteur
  • Mme. Nancy EL RACHKIDY, Maître de Conférences Université Clermont Auvergne, Examinateur
  • M. Mickael MAMAN, Ingénieur de Recherche CEA LETI, Examinateur
  • M. Hervé RIVANO, Professeur INSA Lyon, Directeur de thèse
  • M. Razvan STANICA, Maître de Conférences INSA Lyon, Co-directeur de thèse

CITI seminar – Jean Marie GORCE (Citi) – 07/07 at 14:00

Title: On computing individual exposure risk to a pandemia with BLE-RSSI measures

Date and Place: 07 / 07 / 2020 14:00

Speaker: Jean Marie GORCE (Citi)

Place: https://bbb.tuxlab.net/b/mal-eef-nrd

 

Abstract: Tracking how Covid-19 spreads over a population is a critical aspect that may aid relaxing lockdown conditions. Most of current solutions (e.g. as proposed in the European project PEPP-PT, the French application Stopcovid, the GAEN (Google-Apple Exposure Notification) ) rely on the RSSI signals obtained with Bluetooth Low Energy (BLE) HELLO messages. In this talk, we describe an algorithm which is complient with the constraints imposed by the rules and parameters of the ROBERT protocol developed by the Privatics team (Inria) and used in the Stopcovid application. Underlying the algorithm is mathematical modeling of the physical wireless communication link, based on real-life BLE RSSI traces. In particular, the algorithm is evaluated on experimental data obtained in the PEPP-PT project (April 2020) and lead by the Fraunhoffer institute, and experimental data aquired by the Stopcovid consortium (May, 2020) lead by Inria, both providing a large number of device-to-device BLE RSSI traces in realistic scenarios.