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.

 

 


CITI seminar – Mario Zanon (IMT Lucca) – 30/06 at 14:00

Title: Optimal Control, MPC, and Reinforcement Learning

Date and Place: 30 / 06 / 2020 14:00

Speaker: Mario Zanon (Assistant Professor, IMT Lucca, Italy)

Host: Maracas

 

Abstract: Data-driven control approaches such as Reinforcement Learning (RL) mitigate the issue of model construction and controller tuning by learning directly the (optimal) control law from data. While stunning results have been obtained, RL cannot provide stability nor safety guarantees. Additionally, while partial information on the system is usually available, it can be hard to use it within RL. Model Predictive Control (MPC) is an advanced control technique able to deal with nonlinear systems subject to constraints. The main idea of MPC is to use a mathematical model of the process to predict its future behavior and minimize a given performance index. The advantages of MPC are numerous, as it makes it relatively easy to handle various difficulties in control design, such as dealing with constraints, nonlinear and hybrid dynamics, etc. One of the main drawbacks of MPC is that control performance is highly dependent on the predictive ability of the model. In this seminar, we will discuss how RL and MPC can be combined with the aim of benefitting from the advantages of each while limiting the drawbacks of both. We will introduce the two techniques and present some recent theoretical results, supported by simulation results.

 

Biography:
Mario Zanon received the Master’s degree in Mechatronics from the University of Trento, and the Diplôme d’Ingénieur from the Ecole Centrale Paris, in 2010. After research stays at the KU Leuven, University of Bayreuth, Chalmers University, and the University of Freiburg he received the Ph.D. degree in Electrical Engineering from the KU Leuven in November 2015. He held a Post-Doc researcher position at Chalmers University until the end of 2017 and is now Assistant Professor at the IMT School for Advanced Studies Lucca. His research interests include numerical methods for optimization, economic MPC, reinforcement learning and optimal control and estimation of nonlinear dynamic systems, in particular for aerospace and automotive applications.

 


PhD Defence: “Autonomous Wireless Sensor Network Architecture for Vehicular traffic monitoring at an Intersection”, Domga Komguem, 6th of July 2020 at 10AM

The defense will take place at the University of Yaoundé I and will be available at https://join.skype.com/EVc3J1aGhASf

Title

Autonomous Wireless Sensor Network Architecture for Vehicular traffic monitoring at an Intersection

 

Abstract

In many countries, because of the limited financial budget, the growth of road infrastructures is low compared to the growth of population and the number of vehicles in urban areas. Such a context does not make the task easy for authorities in charge of the management of transportation systems. The introduction of information and communication technologies (ICT) allows to better address these issues. Vehicular traffic management at intersections has an impact on the traffic jam observed in the whole city. In this thesis, our goal is to propose a low-cost, lightweight and autonomous Wireless Sensors Network (WSN) architecture for vehicular traffic monitoring, especially at an intersection. Vehicular traffic data collected can be used, for instance, for intelligent traffic lights management. In the WSN architecture proposed in the literature for vehicular traffic monitoring, underground sensors are used. In terms of network communication, these architectures are not realistic. Nowadays, surface-mounted sensors are proposed by manufacturers.

The first contribution of this thesis is an experimental characterization of wireless links in a WSN with sensors deployed at the ground level. We evaluate the impact of several parameters like the proximity of the ground surface, the communication frequency and the message size on the link quality. Results show a poor link quality at ground level. Based on the conclusions of the experiments, the second contribution of this thesis is WARIM, a new WSN architecture for vehicular traffic monitoring at an intersection. In WARIM, the sensors deployed on a lane form a multi-hop WSN with a linear topology (LWSN). In this network, all the data are forwarded toward the sink. In a network with such properties, the computation and communication requirements are highest in the neighborhood of the sink. Thus, the third contribution of this thesis is a virtual nodes-based and energy efficient sensors deployment strategy for LWSN. Compared to a uniform deployment, this deployment improves the network lifetime by 40%. In our intersection monitoring application, it is important to correlate the messages generated by a sensor to its position with respect to the intersection. Therefore,the fourth contribution of this thesis is, a centroid-based algorithm for sensors ranking in a LWSN. We evaluate the performance of this algorithm considering a realistic channel model, a uniform deployment, as well as the virtual nodes based-deployment proposed in this thesis. Finally, putting all our contributions together, simulations show that WARIM can be used for reliable and real-time vehicular traffic monitoring at an intersection.

 

Jury

  • Marcel FOUDA, Professor, Université de Yaoundé I, President
  • Thomas DJOTIO, Associate Professor, Université de Yaoundé I, Reviewer
  • Nathalie MITTON, Research Director, INRIA, Reviewer
  • Bernard TOURANCHEAU, Professor, Université Grenoble Alpes, Reviewer
  • André-Luc BEYLOT, Professor, ENSEEIHT Toulouse, Examinator
  • René NDOUNDAM, Associate Professor, Université de Yaoundé I, Examinator
  • Razvan STANICA, HDR, INSA Lyon, Examinator
  • Maurice TCHUENTE, Professor, Université de Yaoundé I, Co-Supervisor
  • Fabrice VALOIS, Professor, INSA Lyon, Co-Supervisor