PhD Defence: “Deep learning based approaches for detection in physical layer wireless multiple access”, Cyrille Morin, Amphitheater, Chappe Building, 22th of July 2021 at 14:00 PM

The defense will be held in the Amphitheater, Chappe Building, and will be streamed live here: link

 

Title

Deep learning based approaches for detection in physical layer wireless multiple access

 

 

Abstract

Current trends point towards an accelerated augmentation of devices with a desire to access the shared radio spectrum, both due to the continued democratisation and capability augmentation of user facing radio devices, such as cellphones, computers, and especially wearables, but also to the deployment of connected objects and sensors. Technology, protocols, and legislation improvements increase the available frequency bands by opening new channels in the GHz range, but the density of devices is nevertheless expected to increase. Multiple access to a shared radio frequency resource leads to situations that are both complex to model, and to tackle with known algorithms, and it is true of detection tasks that arise in the physical layer of a wireless transmission. The class of deep learning algorithms is especially useful in this sort of situation without model, or with non tractable algorithms, as long as a large amount of labelled data is available to train the related neural networks. This thesis aims at adapting the deep learning tool to physical layer detection problems, in successive steps of a decoding chain. First with the problem of detecting the origin of a received packet, starting with hardware fingerprinting of a transmitting device, and extending it to a scenario with multiple active devices at the same time, detecting the set of active devices transmitting an explicit codeword. The next step after origin detection is bit detection, to decode transmitted messages. For that, deep learning is used to learn constellations allowing for an efficient bit detection in a multiple-access scenario, namely the two-user uplink NOMA. Data used to train the networks involved in this thesis are gathered both from simulated models, and from experimental implementations in the FIT/CorteXlab software defined radio test-bed.

 

 

Jury

  • Marco Di Renzo – Research Director – CentraleSupélec – Reviewer
    Symeon Chatzinotas – Professor – University of Luxembourg – Reviewer
    Marwa Chafii – Associate Professor – ENSEA – Examiner
    Catherine Douillard – Professor – IMT Atlantique – Examiner
    Christophe Moy – Professor – Université de Rennes 1 – President
    Leonardo S. Cardoso – Associate Professor – Université de Lyon – Co-supervisor
    Jakob Hoydis – Principal Research Scientist – Nvidia – Co-supervisor
    Jean-Marie Gorce – Professor – Université de Lyon – Supervisor

HDR Defence: “Privacy issues in wireless networks, Every frame you send, they’ll be watching you.”, Mathieu Cunche, 2nd of June 2021 at 2PM

The defense will be streamed live here: link

 

Title

Privacy issues in wireless networks, Every frame you send, they’ll be watching you.

Abstract

A growing number of devices carried by users are equipped with wireless technologies such as Bluetooth and Wi-Fi which allow the seamless exchange of information between devices and the network infrastructure. Because they routinely emit wireless messages carrying identifiers and other technical artifacts in cleartext, these technologies expose users to privacy issues. Focusing on the data included in advertising messages, we identify and analyze the leakage of personal data, and study potential and existing countermeasures. More specifically, we try to answer the following questions: what are the privacy threats associated with wireless networks? Which solutions can be deployed to protect users against these threats? How efficient are current privacy protection implementations? We start by an analysis of privacy features of the two major wireless network standards: Wi-Fi and Bluetooth-Low-Energy. We focus our study on address randomization mechanisms, a recently adopted anti-tracking measure, and identify several issues related to implementation as well as standard specifications. To illustrate the diversity and complexity of the issues affecting these technologies, we present two representative cases of personal data leakage in wireless networks. First, leveraging the reverse-engineering of Continuity, a BLE-based protocol developed by Apple, we uncover a collection of personal data leakages affecting billions of devices worldwide. Finally, we present an abuse of Android Wi-Fi permission that can be used to bypass permissions and to infer personal data such as the location of the device. When confronted with those privacy issues, it becomes necessary to increase user protection by developing privacy-preserving mechanisms but most importantly by correctly implementing existing ones. Furthermore, it appears that standard specifications are key elements of a better protection, and it is thus of utmost importance to promote the integration of privacy protection in these standards.

 

Jury

    • Monsieur Noubir Guevara, Professeur, Northeastern University (Rapporteur)
    • Madame Fischer-Hübner Simone, Professeure, Karlstad University (Rapporteur)
    • Madame Guérin-Lassous Isabelle, Professeure, Université Claude Bernard Lyon 1
    • Monsieur Gorce Jean-Marie, Professeur, INSA-Lyon
    • Monsieur Zuniga Juan-Carlos, SigFox
    • Monsieur Anciaux Nicolas, Directeur de recherche, Inria, Université de Versailles/St-Quentin (Rapporteur)

PhD Defence: “De l’évaluation des performances Wi-Fi à la mobilité contrôlée pour les réseaux de drones”, Remy Grünblatt, 8th of January 2021 at 14:00 PM

The defense will be streamed live here: link

 

Title

De l’évaluation des performances Wi-Fi à la mobilité contrôlée pour les réseaux de drones

 

Abstract

La mobilité dans les réseaux de télécommunications est souvent considérée comme un problème qu’il faut résoudre : un appareil mobile sans fil doit adapter ses paramètres de transmission afin de rester connecté à son ou ses homologues, car le canal évolue avec les mouvements de l’appareil. Les drones, qui sont des véhicules aériens sans pilote, ne font pas exception. En raison de leur grande liberté de mouvements, de leur mobilité tridimensionnelle, et ce dans des environnements aussi nombreux que variés, de leur charge utile limitée et de leurs contraintes énergétiques, et en raison du large éventail de leurs applications dans le monde réel, les drones représentent de nouveaux objets d’étude passionnants dont la mobilité est un défi. Pourtant, la mobilité peut aussi être une chance pour les réseaux de drones, surtout lorsque nous pouvons la contrôler. Dans cette thèse, nous explorons comment la mobilité contrôlée peut être utilisée pour augmenter les performances d’un réseau de drones, en mettant l’accent sur les réseaux IEEE 802.11 et les petits drones multi-rotor. Nous décrivons d’abord comment la mobilité est traitée dans les réseaux 802.11, c’est-à-dire en utilisant des mécanismes d’adaptation de débit, puis nous effectuons l’ingénierie inverse de l’algorithme d’adaptation de débit utilisé dans le chipset Wi-Fi du drone Intel Aero. L’étude de cet algorithme d’adaptation de débit, de manière à la fois expérimentale et par simulation, grâce à son implémentation dans le simulateur de réseau NS-3, permet de le comparer à d’autres algorithmes bien connus. Cette étude met en évidence l’importance de ces algorithmes pour les réseaux de drones, en lien avec leur mobilité, et la différence de comportement de chaque nœud en résultant. Par conséquent, une solution de mobilité contrôlée visant à améliorer les performances du réseau ne peut pas supposer beaucoup du comportement des algorithmes d’adaptation de débits. En outre, les applications des réseaux de drones sont diverses, et il est difficile d’imposer des contraintes de mobilité sans devenir incompatible avec un pan complet d’applications. Nous proposons donc une solution de mobilité contrôlée qui exploite le diagramme de rayonnement de l’antenne des drones. Cet algorithme est évalué grâce à outil de simulation développé pour l’occasion, permettant la simulation d’antennes et de drones, basé sur NS-3. Cette solution, qui fonctionne avec n’importe quel algorithme d’adaptation de débit, est distribuée, et ne nécessite aucune coordination globale ou communication spécifique qui pourrait s’avérer coûteuses. Elle ne nécessite pas non plus un contrôle complet de la mobilité du drone comme le requièrent les solutions de mobilité contrôlée existantes, ce qui rend cette solution compatible avec diverses applications.

 

 

Jury

  • Mme. Nathalie MITTON, Directrice de recherche à INRIA Lille Nord – Europe, Rapportrice
  • Mr. Enrico NATALIZIO, Professeur des universités au Technology Innovation Institute – Abu Dhabi, Rapporteur
  • Mme. Laure GONNORD, Maître de conférences à l’université Lyon 1 Claude Bernard – Lyon, Examinatrice
  • Mr. André-Luc BEYLOT, Professeur des universités à l’ENSEEIHT – Toulouse, Examinateur
  • Mr. Franck ROUSSEAU, Maître de conférences à Grenoble INP-Ensimag – Grenoble, Examinateur
  • Mme. Isabelle GUÉRIN-LASSOUS, Professeure des universités à l’université Lyon 1 Claude Bernard – Lyon, Directrice de thèse
  • Mr. Olivier SIMONIN, Professeur des universités à l’INSA de Lyon, Codirecteur de thèse

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

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

PhD Defence: “Dynamic Heterogeneous Memory Allocation for embedded devices”, Tristan Delizy, Chappe Amphitheater, CITI, 19th of December 2019 at 10h00

Title

Dynamic Heterogeneous Memory Allocation for embedded devices

Abstract

Reducing energy consumption is a key challenge to the realisation of the Internet of Things. While emerging memory technologies may offer power reduction and high integration density, they come with major drawbacks such as high latency or limited endurance. As a result, system designers tend to juxtapose several memory technologies on the same chip. We aim to provide the embedded application programmer with a transparent software mechanism to leverage this memory heterogeneity. This work studies the interaction between dynamic memory allocation and memory heterogeneity. We provide cycle accurate simulation of embedded platforms with various memory technologies and we show that different dynamic allocation strategies have a major impact on performance. We demonstrates that interesting performance gains can be achieved even for a low fraction of memory using low latency technology, but only with a clever placement strategy between memory banks. We propose an efficient strategy based on application profiling in our simulator.

 

Jury

  • Olivier Sentieys, Professeur des Universités, Université de Rennes – Examinateur
  • Cécile Belleudy, Maitre de Conférence HDR, Université de Nice Sophia Antipolis – Rapporteure
  • Lionel Torres, Professeur des Universités, Université de Montpellier – Rapporteur
  • Guillaume Salagnac, Maitre de Conférences, INSA de Lyon – Examinateur, Encadrant
  • Tanguy Risset, Professeur des Universités, INSA de Lyon – Examinateur, Co-directeur de thèse
  • Matthieu Moy, Maitre de Conférences HDR, Université Claude Bernard Lyon 1 – Examinateur, Co-directeur de thèse