CD) for Software Defined Vehicles”

The defense will take place on wednesday 5th february at 2 PM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.

Title
Seamless Continuous Integration / Continuous Delivery (CI/CD) for Software Defined Vehicles

Abstract
Driven by the rapid increase in the number of Electronic Control Units (ECUs), current automotive software systems face growing complexity while advancements in software architecture are well behind. This imbalance has resulted in higher system complexity, important financial costs, and significant challenges in maintaining and deploying new services in vehicles. The thesis explores the potential of adopting Continuous Integration/Continuous Delivery (CI/CD) pipelines for software-defined vehicles, focusing on several critical aspects: secure software deployment, adaptability of in-vehicle software, and optimization of performance using edge computing.

The contributions of the thesis are manifold: (1) A comprehensive taxonomy of key findings related to the transformation of automotive ICT systems, (2) A proposal for a blockchain-based multi-automaker software store to manage updates and dependencies, (3) The development of a virtualization framework for multi-microcontroller systems and an evaluation of these OS-level virtualization solutions for in-vehicle systems, (4) A software orchestration framework that prioritizes criticality and optimizes resource allocation in heterogeneous environments, and finally (5) A consensus algorithm to efficiently offload functions to edge-computing IoT nodes, optimizing resource use in automotive cloud-edge systems.

By addressing these issues, the thesis contributes to the future of automotive ICT systems, proposing innovative methods that strike a balance between flexibility and performance in managing software complexity within the evolving landscape of connected, autonomous vehicles.

Jury

Sara BOUCHENAK, Professeure des Universités à L’INSA de Lyon
Diala NABOULSI, Professeure Associée à l’ETS Montréal, rapporteuse
Thierry DELOT, Professeur des Universités à l’Université Polytechnique Hauts-de-France, rapporteur
Hadi TABATABAEE, Professeur Assistant à l’UCD Dublin Jean-Marc MENAUD
Professeur des Universit ́es à l’IMT Atlantique
Frédéric LE MOUËL, Professeur des Universités à l’INSA de Lyon, Directeur de Thèse
Tista LIN, Architecte Logicielle à STELLANTIS, Co-encadrante de thèse

PhD defense Jesus Argote-Aguilar : “Powering low-power Wake-up Radios with RF energy harvesting”

The defense will hold on monday, December 16th, at 9.30 AM in room ,020G at ENSSAT Lannion

Title
Powering low-power Wake-up Radios with RF energy harvesting.

Abstract
Due to the massive deployment of connected devices in the context of the Internet of Things (IoT), powering them exclusively with cables or batteries is not efficient. This thesis explores the use of radiofrequency (RF) energy as an alternative power source for wake-up radios (WuRx) in wireless sensors, thereby reducing their reliance on batteries. The first challenge is to develop an RF energy harvesting circuit capable of providing a regulated voltage from low power levels. An innovative solution is proposed, based on Schottky diode RF rectifiers incorporating the inductive technique. This circuit ensures the operation of an energy management system that powers a semi-active WuRx and stores excess energy when higher power levels are available.

Given the intermittent nature of RF energy, the second challenge is to adapt the WuRx’s energy consumption by modulating its quality of service, defined as the percentage of processed signals among those received, based on the harvested energy.

Jury
* Nathalie DELTIMPLE, Professor at Bordeaux INP, Reviewer
* Christian VOLLAIRE, Professor at Ecole Centrale Lyon, Reviewer
* Daniela DRAGOMIRESCU, Professor at INSA de Toulouse, Examiner
* Laurent CLAVIER, Professor at IMT Nord Europe, Examiner
* Dominique MORCHE, Research Director at CEA-LETI, Invited
* Matthieu GAUTIER, Professor at Univ. de Rennes,Thesis Director
* Guillaume VILLEMAUD, Assoc. Prof. at INSA de Lyon, INSA de Lyon,Thesis Co-Director
* Olivier BERDER, Professor at Univ. de Rennes,Supervisor
* Florin-Doru HUTU, Assoc. Prof. at INSA de Lyon, INSA de Lyon,Supervisor

PhD defense Alix Jeannerot : « Uplink Resource Allocation Methods for Next-Generation Wireless Networks »

The defense will take place on Monday December 16 at 14h in the Amphi Huma Ouest at Insa Lyon.

Title
Uplink Resource Allocation Methods for Next-Generation Wireless Networks

Abstract
Facing the diversity of communication needs of 5G networks and the future 6G, resource allocation is considered as a key enabler to increase the number of devices, the data rate or the reliability of the communication links. In MTC networks, recent work has proposed to adapt the temporal resource allocation as a function of the underlying process
driving the activity of the devices. This thesis firstly focuses on the impact of having only limited knowledge of the underlying process, and proposes methods to mitigate the bias induced by the lack of knowledge.
Secondly, an algorithm for the joint optimization of the temporal resource allocation and the transmit power of the devices is proposed. The algorithm ensures that devices that are likely to transmit on the same resources do so with a sufficient power diversity to ensure their decodability by the BS. Finally, in networks with an eMBB objective, we
propose to jointly optimize the power, the frequency resources used, as well as the number of parallel data streams used by the devices. Our simulation study shows that our joint optimization outperforms current 5G baselines for which these parameters are common to all devices of the cell.

Jury
* LOSCRI Valeria, Directrice de Recherche, Inria Lille, Rapporteur
* LIVA Gianluigi, Chercheur, German Aerospace Center, Rapporteur
* POPOVSKI Petar, Professeur, Aalborg University, Examinateur
* FIJALKOW Inbar, Professeure, ENSEA, Examinatrice
* VALCARCE Alvaro, Ingénieur de Recherche, Nokia Bell Labs, Examinateur
* ADJHI Cédric, Chargé de Recherche, Inria Saclay, Examinateur
* GORCE Jean-Marie, Professeur, INSA Lyon, Directeur de thèse
* EGAN Malcolm, Chargé de Recherche, Inria Lyon, Co-encadrant

PhD defense Thomas Lebrun : “Health Data: Exploring Emerging Privacy Enhancing Mechanisms”

The defense will take place the 5th december at 9 AM at the library Marie-Curie INSA-Lyon

Title
Health Data: Exploring Emerging Privacy Enhancing Mechanisms

Abstract
Health data represents a large volume of information, generated daily and sensitive by nature. However, sharing this data is essential for advancing research and, ultimately, improving patient care. The use of medical data faces limitations due to its sensitivity and the need to ensure confidentiality, which is governed by current regulations. This
necessitates enhanced protection. Interest in alternatives to sharing raw data, such as pseudonymization or anonymization, is increasing alongside the growing need for access to training data for the use of artificial intelligence, which requires large amounts of data to function effectively as a medical assistant.

In this thesis, we explore new privacy-preserving mechanism made possible by the rapid advancements in artificial intelligence. More specifically, my analysis focuses on improving alternatives to the centralization of sensitive data: federated learning, a decentralized method of training artificial intelligence models that do not need sensitive data sharing, as well as synthetic data generation, which creates artificial data similar statistical properties to real data.
Given the lack of consensus on evaluating the privacy of these new approaches, our work focuses on the systematic measurement of privacy leakage and the balance with the utility of synthetic data or the federated learning model. My contributions include a mechanism to enhance the privacy properties of federated learning, as well as a new method for conditional synthetic data generation. This thesis aims to contribute to the development of more robust frameworks for the secure sharing of health data, in compliance with regulatory requirements, thereby facilitating innovations in healthcare.

Jury
* Sonia BEN MOKHTAR, Directrice de Recherche, CNRS/INSA-Lyon, Examiner,
*Szilvia LESTYAN, Docteure-Ingénieure de Recherche, INRIA, Examiner,
* Jérémie DECOUCHANT, Professeur des universités, Université de Delft, Examiner,
* Benjamin NGUYEN, Professeur des universités, INSA-CVL,Thesis Reviewer,
* Emmanuel VINCENT, Directeur de Recherche, INRIA,Thesis Reviewer,

HDR defense Antoine Boutet : “Privacy issues in AI and geolocation: from data protection to user awareness”

The defense will take place on december 10th at 1:30 PM.

Title
Privacy issues in AI and geolocation: from data protection to user awareness

Abstract
The evolution of digital technologies and their increasing adoption have opened major opportunities, highly beneficial for society in general and for individuals in particular. However, it also poses considerable threats to privacy that require appropriate legal and ethical rules. Privacy is essential to protect individuals, for example against possible misuse of personal data. Privacy is also essential to protect society, as shown by the misuse of personal data to influence voters
during elections (e.g., Cambridge Analytica).
In this context of ultra-rapid development of technologies (often deployed before being regulated), my research work is focused on privacy protection. More precisely, I mainly contribute to the field by proposing technical solutions to privacy (by quantifying risks or proposing countermeasures for example), and also through transdisciplinary activities. Indeed, privacy issues cannot be solved by technology alone because they also raise legal, ethical, economic and societal questions that require a dialogue with people from different disciplines.
My main contributions cover 1) issues related to the collection, exploitation and protection of location data, and more recently 2) security and confidentiality of AI. In this second axis, I focused on “privacy considerations in ML”, i.e., the identification of risks related to ML technologies and countermeasures, and “exploiting ML for confidentiality”, using the capabilities of these new tools to protect individuals (with the use of language models for the anonymization of
medical reports for example).
To address these growing privacy issues, it is necessary to quantify the new risks fueled by new technologies and new usages, and to improve the safeguarding of users’ personal information by developing protection mechanisms. Finally, it is also necessary to both raise awareness among end users about the different risks in order to enable them to adapt
their use, and to collaborate with key players in the field to adopt best practices.

Jury
* Pr. Anne-Marie Kermarrec, EPFL
* Pr. Romain Rouvoy, Université de Lille (rapporteur)
* Dr. Aurélien Bellet, Inria (rapporteur)
* Dr. Catusci Palamidessi, Inria (rapporteuse)
* Pr. François Taiani, Université de Rennes 1
* Pr. Sébastien Monnet, Université Savoie Mont-Blanc
* Pr. Eddy Caron, Université Lyon 1
* Dr. Sonia Ben Mokhtar, CNRS, Insa-Lyon

PhD Defence: “Exact and anytime heuristic search for the Time Dependent Traveling Salesman Problem with Time Windows”, Romain Fontaine, Amphi Chappe/Lamarr Building, 9th of June 2023 at 10 AM

The defense will take place on Tuesday 9th June (morning) in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.

Title

Exact and anytime heuristic search for the Time Dependent Traveling Salesman Problem with Time Windows

Abstract

The Time Dependent (TD) Traveling Salesman Problem (TSP) is a generalization of the TSP which allows one to take traffic conditions into account when planning tours in an urban context: travel times between points to visit depend on departure times instead of being constant. The TD-TSPTW further generalizes this problem by adding Time Window constraints, i.e., constraints on visit times. Existing exact approaches such as Integer Linear Programming and Dynamic Programming usually do not scale well; heuristic approaches scale better but provide no guarantees on solution quality.

In this thesis, we introduce a new exact and anytime solving approach for the TD-TSPTW which aims at quickly providing approximate solutions and gradually improving them until proving optimality. We first show how to reduce the TD-TSPTW to the search for a best path in a state-transition graph. We provide an overview of existing search algorithms, with a focus on exact and anytime extensions of A*, and introduce a new one by hybridizing two of them. We show how to combine these exact and anytime search algorithms with local search – in order to faster find solutions of higher quality – and with bounding and time window constraint propagation – in order to filter the search space. Finally, we provide extensive experimental results to (i) validate our main design choices, (ii) compare our approach to state-of-the-art solving approaches on various TD benchmarks with different degrees of realism and different temporal granularities and (iii) compare TD solving approaches to recent TSPTW solvers on constant benchmarks. These experimental results show us that our approach offers a good compromise between the time needed to find good solutions and the time needed to find optimal solutions and prove their optimality for both TD and constant TSPTW instances.

Jury

- - Cédric PRALET, Directeur de Recherche, ONERA – Rapporteur
  - Pierre SCHAUS, Professeur des Universités, UC Louvain – Rapporteur
  - Romain BILLOT, Professeur des Universités, IMT Atlantique – Examinateur
  - Christine SOLNON Professeure des Universités, INSA Lyon – Directrice de thèse
  - Jilles S. DIBANGOYE, Maître de Conférences HDR, University of Groningen – Co-directeur de thèse

HDR Defence: “Contributions to Wireless Sensor Networks for Air Quality Monitoring”, Walid Bechkit, 24nd of May 2021 at 10AM, Lamarr Building, Insa-Lyon

The defense will take place on Friday 24th May at 10AM in the Heidi Lamarr building, Insa-Lyon, Villeurbanne.

Title

Contributions to Wireless Sensor Networks for Air Quality Monitoring

Abstract

In this talk, I will present a summary of my research, which revolves around the design and evaluation of novel solutions for Wireless Sensor Networks to efficiently monitor physical phenomena. I have addressed several scientific and technical issues by adopting a global methodology combining theoretical solutions and experimental developments. Although our solutions can be easily adapted to different applications, the focus was on air quality monitoring, a major societal challenge where new low-cost sensing technologies offer a significant advantage over traditional solutions.

This talk focuses on our main contributions in this area of low-cost sensor networks for environmental monitoring. It is structured around three axes: i) static sensor networks for air quality monitoring in cities and on industrial sites; ii) participatory sensing of air quality and Urban Heat Islands; and iii) UAV fleets for monitoring highly dynamic phenomena. The common thread running through all our solutions is that they take into account both the physical domain knowledge and the characteristics of low-cost sensors such as the limited and heterogeneous measurement accuracy. I will conclude this talk by discussing some personal feedback and setting out some future perspectives.

Jury

- Aline CARNEIRO VIANA, Directrice de recherche, INRIA, Reviewer
- Andrzej DUDA, Professeur, Grenoble INP – Ensimag, Reviewer
- Nathalie MITTON, Directrice de recherche, INRIA, Reviewer
- André-luc BEYLOT, Professeur, Toulouse INP – ENSEEIHT, Examiner
- Abdelmadjid BOUABDALLAH, Professeur, Université de Technologie de Compiègne, Examiner
- Isabelle GUERIN-LASSOUS, Professeur, Université de Lyon 1, Examiner
- Hervé RIVANO, Professeur, INSA-Lyon, Examiner (« Garant »)
- Mouloud KOUDIL, Professeur, ESI-Alger, Guest Examiner

PhD Defence: “Programming language abstractions for the Internet of Things era”, Patrik Fortier, East amphitheater of the humanities building, 22th of May 2024 at 10 AM

The defense will take place on Tuesday 22th May at 10 AM in the East amphitheater of the humanities building, Insa-Lyon, Villeurbanne.

Title

Programming language abstractions for the Internet of Things era

Abstract

Les défis par l’Internet des objets (IoT) exigent des applications modernes qu’elles gèrent d’importants volumes de données provenant de réseaux de capteurs, qui sont ensuite traités, stockés et analysés. Les développeurs ont adopté l’architecture microservices pour répondre aux problèmes passage à l’échelle et faciliter un processus de livraison rapide des logiciels. Cependant, de nouveaux paradigmes tels que le Fog et l’Edge computing introduisent diverses ressources et configurations, ce qui oblige les développeurs à s’adapter à des environnements et des écosystèmes de plus en plus complexes. L’émergence des modèles Function-as-a-Service et Serverless a mis l’accent sur une simplification du code. Cependant, cela soulève des problématiques lorsque les développeurs créent désormais des applications pour des infrastructures sur lesquelles ils n’ont qu’un contrôle limité. Dans les environnements à ressources limitées tels que l’edge computing, les applications sont en concurrence pour les ressources. Par conséquent, les développeurs ont besoin d’outils adaptés avec des abstractions appropriées pour relever les défis modernes tout en réduisant la complexité des applications.

Dans cette thèse, nous présentons des abstractions de langage de programmation adaptées au développement de logiciels distribués à l’ère de l’Internet des Objets. Nous avons consolidé ces abstractions dans un framework qui permet la construction d’applications distribuées de type “data flow” sous la forme de microservices, le tout dans le même code source. Ce framework abstrait à la fois l’infrastructure sous-jacente sur laquelle les applications s’exécutent et la communication entre les services. Nous démontrons que notre approche n’introduit pas de surcoût encombrante et la comparons avec les plateformes Function-as-a-Service de l’industrie.

Pour offrir un contrôle précis sur l’infrastructure, nous introduisons des primitives de langage et un moteur d’exécution local qui gère les informations contextuelles sur le cluster. En outre, nous introduisons l’entropie en tant que métrique de placement innovante pour les applications. Les développeurs peuvent dicter la manière dont ils souhaitent que leur application soit positionnée dans le cluster et comment elle doit répondre à des scénarios tels que la contention des ressources entre les applications partageant la même infrastructure. Ces techniques permettent à l’utilisateur de définir une politique de placement dynamique avec un haut niveau de granularité dans un environnement dont il n’a pas forcément le contrôle total.

Jury

- - Mme Stéphanie CHOLLET, Maître de conférences HDR – ESISAR Grenoble INP – Rapporteure
  - M. Stéphane DUCASSE, Directeur de Recherche – INRIA – Rapporteur
  - M. Philippe ROOSE, Professeur des Universités – Université de Pau et des Pays de l’Adour – Examinateur
  - M. Yannick LOISEAU, Maître de Conférences – Université Clermont Auvergne – Examinateur
  - M. Frédéric LE MOUËL, Professeur des universités – INSA Lyon – Directeur de thèse
  - M. Julien PONGE, Docteur – Red Hat – co-encadrant de thèse

PhD Defence: “Navigation Among Movable Obstacles (NAMO) Extended to Social and Multi-Robot Constraints”, Benoit Renault, Amphi Chappe/Lamarr Building, 19th of December 2023 at 2 PM

The defense will take place on Tuesday 19th December at 2 PM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.

Title

Navigation Among Movable Obstacles (NAMO) Extended to Social and Multi-Robot Constraints

Abstract

As robots become ever more commonplace in human environments, taking care of ever more tasks such as cleaning, security or food service, their current limitations only become more apparent. One such limitation is of their navigation capability in the presence of obstacles: they always avoid them, and freeze in place when avoidance is impossible.

This is what brought about the creation of Navigation Among Movable Obstacles (NAMO) algorithms, expected to allow robots to manipulate obstacles as to facilitate their own movement. However, these algorithms were designed under the hypothesis of a single robot per environment, biasing NAMO algorithms into only optimizing the single robot’s displacement cost – without any consideration for humans or other robots. While it is desirable to endow robots with the human capability of moving obstacles, they must however do so while respecting social norms and rules of humans.

We have thus extended the NAMO problem as to take into account these new social and multi-robots aspects. By relying on the concept of affordance spaces, we have developed a social occupation cost model allowing the evaluation of the impact of moved objects on the environment’s navigability. We implemented (and improved) reference NAMO algorithms, in our open source simulation tool, and modified them so that they may plan compromises between robot displacement cost and social occupation cost of moved obstacles – resulting in improved navigability. We also developed an implicit coordination strategy allowing the concurrent execution of these same algorithms by multiple robots as is, without any explicit communication requirements, while preserving the no-collision guarantee; verifying the relevance of our social occupation cost model in the actual presence of other robots. As such, this work constitutes the first steps towards a Social and Multi-Robot NAMO.

Jury

- Philippe Mathieu , Professeur des Universités, Université de Lille, CRISTAL, Rapporteur
- Fabien Michel, Maître de Conférences HDR, Université Montpellier 2, LIRMM, Rapporteur
- Julie Dugdale, Professeur des Universités, Université de Grenoble, LIG, Examinatrice
- Rachid Alami, Directeur de Recherche CNRS émérite, LAAS, Toulouse, Examinateur
- Olivier Simonin, Professeur des Universités, INSA-Lyon, CITI, Directeur de thèse
- Jacques Saraydaryan, Enseignant Chercheur, CPE Lyon, CITI, Co-encadrant

PhD Defence: “Human and Network Mobility Management using Mobile Phone Data”, Solohaja Rabenjamina, Amphi Chappe/Lamarr Building, 29th of September 2023 at 2 PM

The defense will take place on Friday 29th December at 2 PM in the Heidi Lamarr building (Amphi Chappe), Insa-Lyon, Villeurbanne.

Title

Human and Network Mobility Management using Mobile Phone Data

Abstract

Over the past decade, the increasing use of smartphones has led to a significant rise in the volume of data exchanged through mobile networks of telecommunications operators. Each new generation of mobile network generates more data than its predecessor. By 2027, it is estimated that 289 EB of data will be exchanged per month, with 62% originating from the 5G mobile network. This vast availability of data has opened up new research perspectives, particularly in the study of mobility. Mobile data enables studies on larger populations and extended geographical areas.

In this thesis, we demonstrate that the events described in mobile data can also be found in other data sources. Through comparisons between mobile data and sensors detecting human presence, we observe a reasonable correlation. However, certain events, such as synchronization of peak presence or end-of-day activity, exhibit less similarity. We also utilize mobile data to examine the impact of the COVID-19 lockdowns imposed by the French government on land usage in Paris. Our findings indicate that the first lockdown had a profound impact on mobility patterns and land utilization, while the second and third lockdowns had a lesser impact. Lastly, we leverage this data for the reconfiguration of the mobile network in managing user micro-mobility, known as handover. The eNodeBs, which constitute the access network, can have different profiles and categories. By distinguishing between mobile and stationary users, we can optimize resource allocation through network reconfiguration. Dynamic network reconfiguration, employing various eNodeB profiles, also enables resource savings for mobile users.

Jury

- Marco FIORE, Directeur de Recherche, IMDEA Networks, Rapporteur
- Vania CONAN, Habilité à Diriger des Recherches, Thales, Rapporteur
- Aline CARNEIRO VIANA, Directeur de Recherche, INRIA, Examinatrice
- Sahar HOTEIT, Maître de Conférences, Université Paris Saclay, Examinatrice
- Stefano SECCI, Professeur des Universités, CNAM, Examinateur
- Hervé RIVANO, Professeur des universités, INSA-Lyon, Directeur de thèse
- Razvan STANICA, Maître de conférences HDR, INSA-Lyon, Co-directeur de thèse