Contributions to the Optimized Deployment of Connected Sensors on the Internet of Things Collection Networks Sami Mnasri

Contributions to the optimized deployment of connected sensors on the Internet of Things collection networks Sami Mnasri To cite this version: Sami Mnasri. Contributions to the optimized deployment of connected sensors on the Internet of Things collection networks. Networking and Internet Architecture [cs.NI]. Université Toulouse le Mirail - Toulouse II, 2018. English. NNT : 2018TOU20046. tel-02447194 HAL Id: tel-02447194 https://tel.archives-ouvertes.fr/tel-02447194 Submitted on 21 Jan 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. THÈSE En vue de l’obtention du DOCTORAT DE L’UNIVERSITÉ DE TOULOUSE Délivré par : Université Toulouse - Jean Jaurès Présentée et soutenue par : Sami MNASRI le mercredi 27 Juin 2018 Titre : Contributions to the optimized deployment of connected sensors on the Internet of Things collection networks École doctorale et discipline ou spécialité : ED MITT : Domaine STIC : Réseaux, Télécoms, Systèmes et Architecture Unité de recherche : Institut de Recherche en Informatique de Toulouse (IRIT), équipe RMESS Directeur de Thèse : Thierry VAL, Professeur à l'Université de Toulouse Jury : Pr. Belhassen ZOUARI, Université de Carthage, Rapporteur HDR Dr. Hanen IDOUDI, Ecole Nationale des Sciences de I'nformatique de Tunis, Rapporteur Dr. Najeh NASRI, Ecole Nationale d'Ingénieur de Sfax, Co-Directeur de thèse HDR Dr. Adrien VAN DEN BOSSCHE, l'Université de Toulouse, Co-Encadrant de thèse Nothing is stronger than a high-quality education system that can be the basis of building human being in order to reform morals towards modern civilizations and nations Sami Mnasri To my mother and my father, To my sisters and my brother, To Ameni, To my friends, In testimony of my great love and my strong gratitude Sami Acknowledgements I would first like to thank Pr. Thierry VAL, my thesis director for his support and contribution in this research as well as for his availability. In particular, I thank him for giving me a great freedom in my research, for being attentive to all my comments or ideas and for answering my many questions. His encouragement and advices were invaluable, without which this work would probably not have been possible. I thank Dr Nejah NASRI for his interest in my work as well as for his comments and advices which allowed me to improve the substance and form. I would like to thank Dr. Adrien VAN DEN BOSSCHE for his advice and guidelines, especially at the level of experiments and prototyping that have enhanced my work. I also thank Pr. Belhassen ZOUARI and Dr. Hanen IDOUDI for agreeing to be part of my evaluation committee. My thanks also go to all the teachers, staff and my students on the IUT of Blagnac for the good atmosphere they created in the premises of the IUT. Finally, I thank all the teachers who contributed to my training and to all those who helped me during this work. About this dissertation IoT collection networks raise many optimization problems, in particular because the sensors have limited capacity in energy, processing and memory. We are interested in a global contribution related to the optimization on wireless sensor networks using heuristics, meta- heuristics, hybrid methods, mathematical models, assessments of new optimization approaches on wireless sensor networks, multi-objective models, etc. The continuous evolution of the IoT collection network research domain and technology allows optimizing the use of these networks in different number of contexts. The general problem of deployment of nodes can be described as follows: often, the sensors constituting the network cannot be precisely positioned and are scattered erratically. In order to compensate the randomness of sensor placement and increase the fault tolerance of the network, these sensors can be deployed intelligently. Unlike 3D deployment, there is a very abundant literature on this problem for 2D deployment ranging from the technical specificities of the sensors and their way of communicating, to the topological organization of the network itself. The 3D deployment of wireless sensors poses many optimization problems. Therefore, the goal is to provide 3D sensor organization solutions and find the most optimized architecture in order to improve the network performance. In this context, heuristic approaches for optimization in wireless sensor networks can be envisaged. In addition, optimization problems are generally derived from real problems that are often antagonistic, unmeasurable and include several criteria. The dynamic, distributed and open aspect of the deployment problem led us to adopt advanced modeling and optimization technologies: hybridizing recent meta-heuristics. Indeed, in order to ensure a continuous evolution, a pragmatic flexibility and an infallible robustness against possible disturbances that could affect all or part of the network, the main goal was to propose hybridizations and modifications of the evolutionary optimization algorithms in order to achieve the positioning of nodes in wireless sensor networks with satisfaction of a set of constraints and objectives. In fact, hybrid meta-heuristics exploit the complementarity of these methods with each other, as well as with other "classical" approaches. This new class of hybrid algorithms has proven its robustness and efficiency in solving difficult optimization problems. We propose to focus our contribution composed of several complementary and progressive approaches, on the hybrid meta-heuristics. Especially on the evaluation of their performances and their applications on our real-world problem. These hybridization schemes are all validated by numerical results by the evaluation of algorithms with metrics such as the Hypervolume. Then, simulations supplemented by real experiments on testbeds were proposed. Finally, simulations are confronted with experiments to evaluate the behavior of algorithms and prove their stability and efficiency. Keywords: 3D indoor deployment, ant colony algorithm, dimensionality reduction, experimental validation, genetic algorithms, hybridization, IoT collection networks, meta- heuristics, optimization, particle swarm optimization, user preferences. i Contents Acknowledgements About this dissertation ........................................................................................................................................ i Contents .............................................................................................................................................................. ii List of figures ..................................................................................................................................................... v List of algorithms ............................................................................................................................................. vii List of tables .................................................................................................................................................... viii List of acronyms ............................................................................................................................................... ix Introduction and Overview 1 Motivations and problematic .................................................................................................................... 1 Research aims and principal contributions .............................................................................................. 2 Structuring of the document .................................................................................................................... 4 I State of the Art 5 1 The 3D indoor deployment problem in WSN and DL-IoT collection networks 6 1.1 Introduction ....................................................................................................................................................... 7 1.2 Presentation, migration from WSN to DL-IoT and complexity of the deployment problem .............................7 1.3 The 3D deployment strategies in WSN ............................................................................................................10 1.3.1 Full coverage ........................................................................................................................................... 10 1.3.2 K-coverage ............................................................................................................................................... 11 1.3.3 Surface coverage ...................................................................................................................................... 11 1.4 Conflicting types of deployments ....................................................................................................................11 1.4.1 Single Objective deployment vs. multi-objective deployment ................................................................ 11 1.4.2 Deterministic deployment vs. stochastic deployment .............................................................................

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