The defense will take place on Friday 16th December at 10 AM in the est amphi of the Humanities building, Insa-Lyon, Villeurbanne.

Title

Resilient IoT-based Monitoring System for the Nigerian Oil and Gas Industry

Abstract

Pipeline failures in crude oil transportation occur due to ageing infrastructure, third-party interferences, equipment defects and naturally occurring failures. Consequently, hydrocarbons are released into the environment resulting in environmental pollution, ecological degradation, and unprecedented loss of lives and revenue. Hence, multiple leakage detection and monitoring systems (LDMS) are employed to mitigate such failures. More recently, these LDMS include Wireless Sensor Networks (WSN) and Internet of Things (IoT)-based systems. While they are proven more efficient than other LDMS, many challenges exist in their adoption for pipeline monitoring. These include fault tolerance, energy consumption, accuracy in leakage detection and localisation, and high false alarms, to cite a few.

Therefore, our work seeks to address some of these challenges in implementing IoT-based systems for crude oil pipelines in a resilient end-to-end manner. Specifically, we consider the aspect of accurate leakage detection and localisation by introducing a unique node placement strategy based on fluid propagation for sensitive and multi-sized leakage detection. We also propose a new distributed leakage detection technique (HyDiLLEch) in the WSN layer. It is based on a fusion of existing leakage detection techniques such as the negative pressure wave method, gradient-based method, and pressure point analysis. With HyDiLLEch, we efficiently eliminate single points of failure.

Furthermore, we implement fault-tolerant data and service management in the fog layer utilising the Nigerian National Petroleum Corporation (NNPC) pipeline network as a use case. The problem is modelled as a regionalised data-driven game against nature on the NNPC pipelines. Our proposed regionalised solution (R-MDP) using reinforcement learning optimises accuracy and fault tolerance while minimising energy consumption.

Overall, our system guarantees resiliency to failures and efficiency in terms of detection and localisation accuracy and energy consumption.

Jury

• Stolf, Patricia, Professeur des Universités, Université de Toulouse, Rapporteure
• Guidec, Frédéric, Professeur des Universités, Université Bretagne Sud, Rapporteur
• Menaud, Jean-Marc, Professeur des Universités, IMT-Atlantique, Examinateur
• Caron, Eddy, Maître de Conférences, ENS Lyon, Examinateur
• Takruri-Rizk, Haifa, Professeur des Universités, University of Salford, Examinatrice
• Silva, Bhagya Nathali, Senior Lecturer, University of SriJayawardenepura, Examinatrice
• Le Mouël, Frédéric, Professeur des Universités, INSA Lyon, Directeur de thèse
• Stouls, Nicolas, Maître de Conférences, INSA Lyon, Co-Directeur de thèse
• Yusuf, Kabir, Docteur en gestion des ressources environnementales, PTDF/SAPZ, Invité

The defense will take place on Friday 16th December at 10 AM in the west amphi of the Humanities building, Insa-Lyon, Villeurbanne.

Title

Contributions to the development of passive RFID tag-to-tag communications for the Internet of Things

Abstract

With the emergence of cognitive sensor networks, and in particular the IoT (Internet of Things), passive RFID (Radio Frequency Identification) UHF (Ultra High Frequency) technology is evolving with new functionalities. New types of applications going beyond the classics such as logistics, security and traceability are being developed. Still benefiting from unitary identification, new types of tags, called augmented tags, are appearing integrating new capacities such as environmental sensitivity, cognitive behaviour, data processing, communication between tags, etc. In this context, the objective of this thesis is to propose strategies and methods to optimize communications between tags, called tag-to-tag “tag-to-tag” (T2T) communications. This new type of radio link, between directly communicating tags, relies on the presence of an external radio frequency (RF) source and is based on the principle of retro-modulation. In particular, the scenarios analyzed are projected within the framework of the Spie ICS – INSA Lyon chair, which focuses on the IoT.

This thesis more specifically targets the application domain of UHF RFID for which the concept of T2T communications has been proposed and demonstrated since 2011, but with relatively little work done so far. In a T2T RFID system, two RFID tags (passive or semi- passive) communicate with each other directly without going through the reader. One of the tags plays the role of “reader tag”: in the presence of an RF source (for example an RFID reader) in its vicinity, it emits binary information by retro-modulation (or backscatter by switching charges), by switching the load seen by its antenna on two distinct impedances, thus reflecting two distinct power levels (modulation here considered in amplitude). The other tag plays the role of “receiver tag”: it receives the information transmitted and demodulates it.

In the traditional case of UHF RFID, the reader emits, in accordance with the standard, a modulated signal with a high modulation depth in order to facilitate the detection of the message transmitted by the tag. The tag responds by retro modulation and the signal it returns is then a signal where the two levels of information (in the case of amplitude modulation) are not very distinct and noisy. However, the player’s demodulator is very efficient, based on quadrature synchronous demodulation, it has very good sensitivity. In the case of T2T communication, a fundamental difference is that the detection is here performed by the second tag which is in the vicinity of the reader tag. Consequently, on the one hand, the performance of the receiver (that of a “simple” RFID tag) is much more limited, while the modulated signal is not necessarily at high modulation depth. And on the other hand, the two tags interact. This inter-tag electromagnetic coupling impacts in particular radiation patterns and impedances, and moreover, it depends on the mutual positions of the two tags, more precisely on their antennas (distance separating them, relative orientation, etc.), which leads to high variability the characteristics of the T2T system (and therefore its performance). In addition, there is the impact of the relative position of the external RF source with the pair of tags, which significantly modifies the characteristics of the retro-modulated signals.

The main challenge of the thesis is to determine a framework that can take into account all the factors (signal, component, circuit and system) impacting T2T communication with the aim of evaluating performance, particularly in terms of communication rate. binary error, a metric conventionally used in the field of telecommunications.

Keywords: Backscattering, passive tags, tag to tag communications, UHF RFID

Jury

• Bergeret Emmanuel Professor Université Clermont Auvergne Reviewer
• Breard Arnaud Professor Ecole Centrale de Lyon Examiner
• Lepage Anne-Claire Associate Professor Telecom Paris Examiner
• Lienard Martine Professor Université de Lille Examiner
• Vena Arnaud Associate Professor HDR Université de Montpellier Reviewer
• Villemaud Guillaume Associate Professor HDR INSA-Lyon Thesis director
• Hutu Florin-Doru Associate Professor INSA-Lyon Co-director
• Duroc Yvan Professor Université Claude Bernard Lyon 1 Co-director

The defense will take place on Wenesday, July 6 at 2 pm, in the amphitheatre of the Telecommunications Department (Claude Chappe building), INSA Lyon, Villeurbanne.

The presentation will be available online: https://insa-lyon-fr.zoom.us/j/91599742042

Title

User Association in Flexible and Agile Mobile Networks

Abstract

The user association process in cellular networks consists in choosing the base station with which the user equipment will negotiate radio resources. The current association is based on measuring the signal strength received by the user equipment from each of the base stations. The association must now deal with the diversification of user application needs and the growing heterogeneity of cellular networks.

In this thesis, we show experimentally that the current association process has reached its limits, that it is agnostic of the configurations of the base stations and that it does not allow to control the throughput that the user equipment will obtain following the association. We set up for the realization of our measurements an experimental platform based on the software suite of cellular networks srsRAN and software radios USRP NI-2091.

We propose in this thesis a new metric to be used during the association process. This metric, broadcasted by the base station, is a load information which in our case will be represented by the number of user equipment connected to the base station. We also discuss other metrics that can be used as load information. We show, once again experimentally, by modifying the source code of the srsRAN software suite ourselves, that if the user equipment takes this load information into account in the association process, the association decision is improved in 22% of cases.

Jury

• Tara ALI YAHIA, Associate Professor HDR, Université Paris Saclay (Reviewer)
• Vania CONAN, HDR, Thales (Reviewer)
• Véronique VEQUE, Professor, Université Paris Saclay (Examiner)
• Frédéric LAUNAY, Associate Professor, IUT de Poitiers (Examiner)
• Stéphane FRENOT, Professor, INSA-Lyon (Examiner)
• André-Luc BEYLOT, Professor, ENSEEIHT (Examiner)
• Fabrice VALOIS, Professor, INSA-Lyon (Thesis supervisor)
• Razvan STANICA, Associate Professor HDR, INSA-Lyon (Thesis co-supervisor)

The defense will take place on Thursday, July 7 at 2 pm, in the amphitheatre of the Telecommunications Department (Claude Chappe building), INSA Lyon, Villeurbanne.

The presentation will be available online: https://youtu.be/hX3t9pKPcoc

Title

Activity Models and Bayesian Estimation Algorithms for Wireless Grant-Free Random Access

Abstract

The new 5G’s wireless networks have recently started to be deployed all around the world. With them, a large spectrum of services are about to emerge, resulting in new stringent requirements so that 5G targets performance exceed that of 4G by a factor of 10. The services are centered around the use cases of enhanced mobile broadband (eMBB), ultra reliable and low-latency communication (uRLLC) and massive machine-type communication (mMTC) where each of which has required the ongoing development of key new technologies. Many of these technologies will also play an important role in the emergence of 6G.

In this thesis, the focus is on grant-free RA (GFRA) as an enabler of uRLLC and mMTC. GFRA is a new protocol introduced in 5G new radio (5G-NR) for reducing the data overhead of the random access (RA) procedure. This results in a significant reduction in the latencies of the user equipments (UEs) access to a connected medium via an access point (AP). Achieving efficient GFRA is of key importance for many 5G applications, e.g. for large scale internet of things (IoT) wireless networks. The study of new non-orthogonal multiple access (NOMA) signal processing techniques is then considered. Using tools from the theory of compressed sensing (CS), and particularly from Bayesian CS, new algorithms within the family of approximate message passing (AMP) are developed to address the joint active user detection and channel estimation (AUDaCE) problem. The active user detection is crucial to properly identify transmitting UEs within the context of large-scale dense network; the channel estimation is equally important so that an AP can reliably transmit back data to the detected UEs.

In this thesis, in contrast to existing work on this topic, the AUDaCE is studied for wireless networks where the activity of the UEs is assumed to be correlated, as is typical for many large-scale dense networks. To this end, two new activity models are introduced. The first one assumes that the activity of the UEs in the network can be modeled via group-homogeneous activity (GHomA) where devices in the same group have common pairwise correlations and marginal activity probabilities. The second model accounts for more general dependence structure via group-heterogeneous activity (GHetA). Novel approximate message passing algorithms within the hybrid GAMP (HGAMP) framework are developed for each of the models. With the aid of latent variables associated to each group for modeling the activity probabilities of the UEs, the GHomA-HGAMP algorithm can perform AUDaCE for GFRA leveraging such a group homogeneity. When the activity is heterogenous, i.e. each UE is associated with a latent variable modeling its activity probability correlated with the other variables, it is possible to develop GHetA-HGAMP using the copula theory.

Extensive numerical studies are performed, which highlight significant performance improvements of GHomA-HGAMP and GHetA-HGAMP over existing algorithms (modified generalized AMP (GAMP) and group-sparse HGAMP (GS-HGAMP)), which do not properly account for correlation in activity. In particular, the channel estimation and active user detection capability are enhanced in many scenarios with up to a 4dB improvement with twice less user errors.

As a whole, this thesis provides a systematic approach to AUDaCE for wireless networks with correlated activities using tools from Bayesian CS. We then conclude by showing how it could be used for multi-carrier orthogonal frequency division multiplexing (OFDM) scenarios with possible extensions for grant-free (GF) data transmission leveraging joint data recovery, active user detection and channel estimation (DrAUDaCE).

Jury

• Catherine DOUILLARD, Professor @ IMT Atlantique, Reviewer
• Dejan VUKOBRATOVIC, Professor @ Univ. Novi Sad, Reviewer
• Aline ROUMI, Senior Research Scientist @ INRIA Rennes, Examiner
• Philippe CIBLAT, Professor @ Telecom Paris, Examiner
• Cedomir STEFANOVIC, Professor @ Univ. Aalborg, Examiner
• Jean-Marie GORCE, Professor @ INSA Lyon, Director
• Malcolm EGAN, Research Scientist @ INRIA Lyon, Supervisore