Securing routing protocols in mobile ad hoc networks

A Mobile Ad Hoc Network (MANET) is more prone to security threats than other wired and wireless networks because of the distributed nature of the network. Conventional MANET routing protocols assume that all nodes cooperate without maliciously disrupting the operation of the protocol and do not provide defence against attackers. Blackhole and flooding attacks have a dramatic negative impact while grayhole and selfish attacks have a little negative impact on the performance of MANET routing protocols. Malicious nodes or misbehaviour actions detection in the network is an important task to maintain the proper routing protocol operation. Current solutions cannot guarantee the true classification of nodes because the cooperative nature of the MANETs which leads to false exclusions of innocent nodes and/or good classification of malicious nodes. The thesis introduces a new concept of Self- Protocol Trustiness (SPT) to discover malicious nodes with a very high trustiness ratio of a node classification. Designing and implementing new mechanisms that can resist flooding and blackhole attacks which have high negative impacts on the performance of these reactive protocols is the main objective of the thesis. The design of these mechanisms is based on SPT concept to ensure the high trustiness ratio of node classification. In addition, they neither incorporate the use of cryptographic algorithms nor depend on routing packet formats which make these solutions robust and reliable, and simplify their implementations in different MANET reactive protocols. Anti-Flooding (AF) mechanism is designed to resist flooding attacks which relies on locally applied timers and thresholds to classify nodes as malicious. Although AF mechanism succeeded in discovering malicious nodes within a small time, it has a number of thresholds that enable attacker to subvert the algorithm and cannot guarantee that the excluded nodes are genuine malicious nodes which was the motivation to develop this algorithm. On the other hand, Flooding Attack Resisting Mechanism (FARM) is designed to close the security gaps and overcome the drawbacks of AF mechanism. It succeeded in detecting and excluding more than 80% of flooding nodes within the simulation time with a very high trustiness ratio. Anti-Blackhole (AB) mechanism is designed to resist blackhole attacks and relies on a single threshold. The algorithm guarantees 100% exclusion of blackhole nodes and does not exclude any innocent node that may forward a reply packet. Although AB mechanism succeeded in discovering malicious nodes within a small time, the only suggested threshold enables an attacker to subvert the algorithm which was the motivation to develop it. On the other hand, Blackhole Resisting Mechanism (BRM) has the main advantages of AB mechanism while it is designed to close the security gaps and overcome the drawbacks of AB mechanism. It succeeded in detecting and excluding the vast majority of blackhole nodes within the simulation time

Machine Learning for Unmanned Aerial System (UAS) Networking

Fueled by the advancement of 5G new radio (5G NR), rapid development has occurred in many fields. Compared with the conventional approaches, beamforming and network slicing enable 5G NR to have ten times decrease in latency, connection density, and experienced throughput than 4G long term evolution (4G LTE). These advantages pave the way for the evolution of Cyber-physical Systems (CPS) on a large scale. The reduction of consumption, the advancement of control engineering, and the simplification of Unmanned Aircraft System (UAS) enable the UAS networking deployment on a large scale to become feasible. The UAS networking can finish multiple complex missions simultaneously. However, the limitations of the conventional approaches are still a big challenge to make a trade-off between the massive management and efficient networking on a large scale. With 5G NR and machine learning, in this dissertation, my contributions can be summarized as the following: I proposed a novel Optimized Ad-hoc On-demand Distance Vector (OAODV) routing protocol to improve the throughput of Intra UAS networking. The novel routing protocol can reduce the system overhead and be efficient. To improve the security, I proposed a blockchain scheme to mitigate the malicious basestations for cellular connected UAS networking and a proof-of-traffic (PoT) to improve the efficiency of blockchain for UAS networking on a large scale. Inspired by the biological cell paradigm, I proposed the cell wall routing protocols for heterogeneous UAS networking. With 5G NR, the inter connections between UAS networking can strengthen the throughput and elasticity of UAS networking. With machine learning, the routing schedulings for intra- and inter- UAS networking can enhance the throughput of UAS networking on a large scale. The inter UAS networking can achieve the max-min throughput globally edge coloring. I leveraged the upper and lower bound to accelerate the optimization of edge coloring. This dissertation paves a way regarding UAS networking in the integration of CPS and machine learning. The UAS networking can achieve outstanding performance in a decentralized architecture. Concurrently, this dissertation gives insights into UAS networking on a large scale. These are fundamental to integrating UAS and National Aerial System (NAS), critical to aviation in the operated and unmanned fields. The dissertation provides novel approaches for the promotion of UAS networking on a large scale. The proposed approaches extend the state-of-the-art of UAS networking in a decentralized architecture. All the alterations can contribute to the establishment of UAS networking with CPS

Information dissemination in mobile networks

This thesis proposes some solutions to relieve, using Wi-Fi wireless networks, the data consumption of cellular networks using cooperation between nodes, studies how to make a good deployment of access points to optimize the dissemination of contents, analyzes some mechanisms to reduce the nodes' power consumption during data dissemination in opportunistic networks, as well as explores some of the risks that arise in these networks. Among the applications that are being discussed for data off-loading from cellular networks, we can find Information Dissemination in Mobile Networks. In particular, for this thesis, the Mobile Networks will consist of Vehicular Ad-hoc Networks and Pedestrian Ad-Hoc Networks. In both scenarios we will find applications with the purpose of vehicle-to-vehicle or pedestrian-to-pedestrian Information dissemination, as well as vehicle-to-infrastructure or pedestrian-to-infrastructure Information dissemination. We will see how both scenarios (vehicular and pedestrian) share many characteristics, while on the other hand some differences make them unique, and therefore requiring of specific solutions. For example, large car batteries relegate power saving techniques to a second place, while power-saving techniques and its effects to network performance is a really relevant issue in Pedestrian networks. While Cellular Networks offer geographically full-coverage, in opportunistic Wi-Fi wireless solutions the short-range non-fullcoverage paradigm as well as the high mobility of the nodes requires different network abstractions like opportunistic networking, Disruptive/Delay Tolerant Networks (DTN) and Network Coding to analyze them. And as a particular application of Dissemination in Mobile Networks, we will study the malware spread in Mobile Networks. Even though it relies on similar spreading mechanisms, we will see how it entails a different perspective on Dissemination

Heterogeneous Cellular Networks: From Resource Allocation To User Association

Heterogeneous networking paradigm addresses the ever growing need for capacity and coverage in wireless networks by deploying numerous low power base stations overlaying the existing macro cellular coverage. Heterogeneous cellular networks encompass many deployment scenarios, with different backhauling techniques (wired versus wireless backhauling), different transmission coordination mechanisms and resource allocation schemes, different types of links operating at different bands and air-interface technologies, and different user association schemes. Studying these deployment scenarios and configurations, and understanding the interplay between different processes is challenging. In the first part of the thesis, we present a flow-based optimization framework that allows us to obtain the throughput performance of a heterogeneous network when the network processes are optimized jointly. This is done under a given system ``snapshot'', where the system parameters like the channel gains and the number of users are fixed and assumed known. Our framework allows us to configure the network parameters to allocate optimal throughputs to these flows in a fair manner. This is an offline-static model and thus is intended to be used at the engineering and planning phase to compare many potential configurations and decide which ones to study further. Using the above-mentioned formulation, we have been able to study a large set of deployment scenarios and different choices of resource allocation, transmission coordination, and user association schemes. This has allowed us to provide a number of important engineering insights on the throughput performance of different scenarios and their configurations. The second part of our thesis focuses on understanding the impact of backhaul infrastructure's capacity limitation on the radio resource management algorithms like user scheduling and user association. Most existing studies assume an ideal backhaul. This assumption, however, needs to be revisited as backhaul considerations are critical in heterogeneous networks due to the economic considerations. In this study, we formulate a global $\alpha$-fair user scheduling problem under backhaul limitations, and show how this limitation has a fundamental impact on user scheduling. Using results from convex optimization, we characterize the solution of optimal backhaul-aware user scheduling and show that simple heuristics can be used to obtain good throughput performance with relatively low complexity/overhead. We also study the related problem of user association under backhaul-limitations. This study is a departure from our ``snapshot'' approach. We discuss several important design considerations for an online user association scheme. We present a relatively simple backhaul-unaware user association scheme and show that it is very efficient as long as the network has fine-tuned the resource allocation

Co-conception contrôle / communication pour économiser l'énergie dans les systèmes commandés en réseau sans fil

Energy is a key resource in Networked Control Systems, in particular in applications concerning wireless networks. This thesis investigates how to save energy in wireless sensor nodes with control and communication co-Design. This thesis reviews existing techniques and approaches that are used to save energy from a communication and a control point of view. This review is organized according to the layered communication architecture covering from bottom to top the Physical, Data Link, Network, and Application layers. Then, from the conclusion that the radio chip is an important energy consumer, a joint radio-Mode management and feedback law policy is derived. The radio-Mode management exploits the capabilities of the radio chip to switch to low consuming radio-Modes to save energy, and to adapt the transmission power to the channel conditions. This results in an event-Based control scheme where the system runs open loop at certain time. A natural trade-Off appears between energy savings and control performance. The joint policy is derived in the framework of Optimal Control with the use of Dynamic Programming. This thesis solves the optimal problem in both infinite and finite horizon cases. Stability of the closed loop system is investigated with Input-To-State Stability framework. The main conclusion of this thesis, also shown in simulation, is that cross-Layer design in Networked Control System is essential to save energy in the wireless nodes.L'énergie est une ressource clé dans les systèmes commandés en réseau, en particulier dans les applications concernant les réseaux sans fil. Cette thèse étudie comment économiser l'énergie dans les capteurs sans fil avec une co-Conception contrôle et communication. Cette thèse examine les techniques et les approches existantes qui sont utilisées pour économiser l'énergie d'un point de vue de la communication et du contrôle. Cet étude est organisée selon une architecture de communication par couches couvrant de bas en haut les couches Physique, Liaison, Réseau, et Application. Puis, à partir de la conclusion que la puce radio est un important consommateur d'énergie, une loi conjointe de gestion des modes radio et de contrôle en boucle fermée est établie. La gestion des modes radio exploite les capacités de la puce radio à communter dans des modes de basses consommation pour économiser l'énergie, et d'adapter la puissance de transmission aux conditions du canal. Il en résulte un système de contrôle basé sur des événements où le système fonctionne en boucle ouverte à certains moments. Un compromis naturel apparaît entre l'économie d'énergie et les performances de contrôle. La loi conjointe est établie avec une formulation de contrôle optimal utilisant la Programmation Dynamique. Cette thèse résout le problème optimal dans les deux cas d'horizon infini et fini. La stabilité du système en boucle fermée est étudiée avec la formulation Input-To-State Stability (ISS). La principale conclusion de cette thèse, également illustrée dans la simulation, est que la conception à travers différentes couches dans les systèmes commandés en réseau est essentielle pour économiser l'énergie dans les noeuds sans fil

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

Fundamentals

Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters