The jury deliberated to highlight a junior colleague from our community.
The 2024 jury grants the 2024 Junior Researcher Award of the GDR RSD to Oana Iova.
Oana Iova has been an associate professor at INSA Lyon since 2017 and conducts her research at the CITI laboratory.
For more information : https://gdr-rsd.cnrs.fr/laureate-du-prix-chercheur-junior-2024/
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
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
The defense will take place on november 25 at 9 AM
Title
Learning spatial representations for single-task navigation and multi-task policies
Abstract
Autonomously behaving in the 3D world requires a large set of skills, among which are perceiving the surrounding environment, representing it precisely and efficiently enough to keep track of the past, making decisions, and acting to achieve specified goals. Animals, for instance humans, stand out by their robustness when it comes to acting in the
world. In particular, they can efficiently generalize to new environments but are also able to rapidly master many tasks of interest from a few examples. We will study how artificial neural networks can be trained to acquire a subset of these abilities. We will first focus on training neural agents to perform semantic mapping, both from augmented supervision signal and with proposed neural-based scene representations.
Neural agents are often trained with Reinforcement Learning (RL) from a sparse reward signal. Guiding the learning of scene mapping abilities by augmenting the vanilla RL supervision signal with auxiliary spatial reasoning tasks will help navigate efficiently. Instead of modifying the training signal of neural agents, we will also see how incorporating specific neural-based representations of semantics and geometry within the architecture of the agent can help improve performance in goal-driven navigation. Then, we will study how to explore a 3D environment to build neural representations of space that are as satisfying as possible based on robotic-oriented metrics we will propose. Finally, we will move from navigation-only to multi-task agents, and see how important it is to tailor visual features from
sensor observations to the task at hand to perform a wide variety of tasks, but also to adapt to new unknown tasks from a few demonstrations.
Jury
* Ivan Laptev, Directeur de Recherche (INRIA Paris / MBZUAI), Rapporteur
* Karteek Alahari, Directeur de Recherche (INRIA Grenoble), Rapporteur
* Nicolas Thome, Professeur des Universités (Sorbonne Université), Examinateur
* Georgia Chalvatzaki, Full Professor (TU Darmstadt), Examinatrice
* Laëtitia Matignon, Maître de Conférences (UCBL), Co-Directrice de thèse
* Olivier Simonin, Professeur des Universités (INSA Lyon), Co-Directeur de thèse
* Christian Wolf, Principal Scientist (Naver Labs Europe), Co-Directeur de thèse
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,
On Thursday, October 24th, the teams of the CITI laboratory gathered at La Commune (Lyon 07) for their welcome day. It was an opportunity to review the laboratory’s roadmap and introduce the new faces, including the BioTic team, which will soon be joining the laboratory.
For more information : https://intranet.insa-lyon.fr/actualites/le-laboratoire-citi-fait-sa-rentree
The defense will take place on January 10th, 2025 at 9am in Amphi Est, Humanités Building, INSA Lyon
Title
Deployment of mobile base stations in cellular networks
Abstract
In current commercial mobile networks, we use fixed base stations (FBS) to provide services to users. However, the first step for deploying fixed base stations requires detailed studies to determine the architecture, the location of the base stations, the capacity, their configurations, etc. With the miniaturization of electronic equipment and network functions virtualization, it is now possible to attempt to embed base stations on movable platforms (e.g., drones) that are able to adjust their position when necessary.
For the ANR DEMON project that funds this thesis, the goal is to establish an adaptive mobile network with movable base stations (MBS). The MBS are thus capable of repositioning themselves to adapt to network changes in real-time. First, we need to understand what the use of MBS in mobile networks can bring to users and for telecommunication operator. Therefore, this thesis explores the advantages and limitations of using MBS in mobile networks. We progressively study the use of MBS in three scenarios: a post-disaster emergency area, an urban neighborhood, and an entire city.
In this thesis, we will show that, MBS can adjust their position in real-time based on the mobility of the users and also considering the user demand, MBS can often outperform FBS in terms of performance, with less base stations deployed. For example, when users form a group, if the MBS and FBS have the same transmit power, the MBS can quadruple the user throughput. In addition, we will highlight when the transmit power of MBS is 50 dBm lower than that of FBS, the performance can be comparable.
Traffic demand in mobile networks is evolving in time and space, when using FBS, we need to respond to the highest demand.
Thanks to the flexibility and mobility of MBS, they can be self-deployed only when needed, which reduces costs by about 20%. The use of MBS allows the concept of self-deployable network to become a reality.
Jury
* André-Luc Beylot, Professeur des Universités (ENSEEIHT), Rapporteur
* Véronique Vèque, Professeure des Universités (Université Paris Saclay), Rapporteure
* Walid Dabbous, Directeur de Recherche (Centre Inria d’Université Côte d’Azur), Examinateur
* Xavier Lagrange, Professeur des Universités (IMT Atlantique), Examinateur
* Razvan Stanica, Maître de Conférences HDR (INSA Lyon), Directeur de thèse
* Fabrice Valois, Professeur des Universités (INSA Lyon), Co-Directeur de thèse
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
Josiane Kouam will give a talk in the meeting room at the 4th floor of the Inria building.
Title
Mobile Human Behavior: Availability, Leveraging, and Addressing Privacy Attacks
Abstract
In an era of ubiquitous mobile connectivity, the security and privacy of users have become paramount concerns. My research delves into the intricate balance between harnessing mobile human behavior data for network optimization and security while safeguarding individual privacy from emerging threats. This talk will cover three key facets of my work in mobile security and privacy, offering insights into the opportunities and challenges presented by the pervasive nature of mobile technologies.
First, I will overview the importance and challenge of ensuring privacy-preserving access to such datasets. Next, I will discuss how behavioral data can be leveraged to enhance network security, including the development of a game-theoretic approach to mitigate international bypass frauds in cellular networks. Finally, I will address privacy risks linked to mobile behavioral data, focusing on vulnerabilities in permission-less sensors like accelerometers when combined with publicly available distinctive network properties.
This discussion will provide insights into balancing the benefits and risks of mobile data in an increasingly connected world.
Biography
Anne Josiane Kouam is a post-doctoral researcher at TU Berlin and member of the Machine Learning and Security team. She obtained her PhD from INRIA Saclay and Ecole Polytechnique, France, in May 2023. With a focus on privacy and security in Mobile and Cellular networks, her research investigates the evolving landscape of threats, particularly those arising from the intersection of Machine Learning and network security.
The RoboCup, an international robotics competition, will be held from July 17 to 21 in the Netherlands. The LyonTech team (CPE, INSA Lyon, Inria, PaloIT), some members of which are part of the Chroma project team, will participate in the @Home category (in the Open Platform league). Their goal: to design a companion robot capable of assisting humans in the daily challenges of the home. It is one of the world’s most important technological events for robotics research and education.
Good luck to the team!
More information : https://robocup-lyontech.github.io/opl/