GAME THEORETIC FLOW AND ROUTING CONTROL FOR COMMUNICATION NETWORKS

As the need to support high speed data exchange in modern communication networks grows rapidly, effective and fair sharing of the network resources becomes very important. Today's communication networks typically involve a large number of users that share the same network resources but may have different, and often competing, objectives. Advanced network protocols that are implemented to optimize the performance of such networks typically assume that the users are passive and are willing to accept compromising their own performance for the sake of optimizing the performance of the overall network. However, considering the trend towards more decentralization in the future, it is natural to assume that the users in a large network may take a more active approach and become more interested in optimizing their own individual performances without giving much consideration to the overall performance of the network. A similar situation occurs when the users are members of teams that are sharing the network resources. A user may find itself cooperating with other members of its team which itself is competing with the other teams in the network. Game theory appears to provide the necessary framework and mathematical tools for formulating and analyzing the strategic interactions among users, or teams of users, of such networks. In this thesis, we investigate networks in which users, or teams of users, either compete or cooperate for the same network resources. We considered two important network topologies and used many examples to illustrate the various solution concepts that we have investigated.. First we consider two-nodeiiiparallel link networks with non-cooperative users trying to optimally distribute their flows among the links. For these networks, we established a condition which guarantees the existence and uniqueness of a Nash equilibrium for the link flows. We derived an analytical expression for the Nash equilibrium and investigated its properties in terms of the network parameters and the users preferences. We showed that in a competitive environment users can achieve larger flow rates by properly emphasizing the corresponding term in their utility functions, but that this can only be done at the expense of an increase in the expected delay. Next, we considered a general network structure with multiple links, multiple nodes, and multiple competing users. We proved the existence of a unique Nash equilibrium. We also investigated many of its intuitive properties. We also extended the model to a network where multiple teams of users compete with each other while cooperating within the teams to optimize a team level performance. For this model, we studied the Noninferior Nash solution and compared its results with the standard Nash equilibrium solution

Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

A Comprehensive Survey of Potential Game Approaches to Wireless Networks

Potential games form a class of non-cooperative games where unilateral improvement dynamics are guaranteed to converge in many practical cases. The potential game approach has been applied to a wide range of wireless network problems, particularly to a variety of channel assignment problems. In this paper, the properties of potential games are introduced, and games in wireless networks that have been proven to be potential games are comprehensively discussed.Comment: 44 pages, 6 figures, to appear in IEICE Transactions on Communications, vol. E98-B, no. 9, Sept. 201

Quadri-dimensional approach for data analytics in mobile networks

The telecommunication market is growing at a very fast pace with the evolution of new technologies to support high speed throughput and the availability of a wide range of services and applications in the mobile networks. This has led to a need for communication service providers (CSPs) to shift their focus from network elements monitoring towards services monitoring and subscribers’ satisfaction by introducing the service quality management (SQM) and the customer experience management (CEM) that require fast responses to reduce the time to find and solve network problems, to ensure efficiency and proactive maintenance, to improve the quality of service (QoS) and the quality of experience (QoE) of the subscribers. While both the SQM and the CEM demand multiple information from different interfaces, managing multiple data sources adds an extra layer of complexity with the collection of data. While several studies and researches have been conducted for data analytics in mobile networks, most of them did not consider analytics based on the four dimensions involved in the mobile networks environment which are the subscriber, the handset, the service and the network element with multiple interface correlation. The main objective of this research was to develop mobile network analytics models applied to the 3G packet-switched domain by analysing data from the radio network with the Iub interface and the core network with the Gn interface to provide a fast root cause analysis (RCA) approach considering the four dimensions involved in the mobile networks. This was achieved by using the latest computer engineering advancements which are Big Data platforms and data mining techniques through machine learning algorithms.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

Cell sectoring for CDMA cellular systems.

Shen Fangzhong.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references (leaves 55-57).Abstracts in English and Chinese.Abstract --- p.iAcknowledgements --- p.iiiList of Figures --- p.viList of Tables --- p.ixChapter Chapter 1. --- Introduction --- p.1Chapter 1.1. --- Motivation --- p.1Chapter 1.2. --- Related Work --- p.2Chapter 1.3. --- Our Work --- p.2Chapter 1.4. --- Some Assumptions --- p.2Chapter 1.4.1. --- Beamforming --- p.2Chapter 1.4.2. --- Downlink Channel --- p.2Chapter 1.4.3. --- Single Cell --- p.3Chapter 1.5. --- Thesis Road Map --- p.3Chapter Chapter 2. --- Preliminaries of Cell Sectoring --- p.4Chapter 2.1. --- Introduction --- p.4Chapter 2.2. --- Beamforming --- p.4Chapter 2.2.1. --- Linear Array --- p.5Chapter 2.2.2. --- Circular Array --- p.8Chapter 2.2.3. --- Butler Beamforming Network --- p.9Chapter 2.2.4. --- Dynamic Beamforming --- p.10Chapter 2.3. --- Power Control --- p.16Chapter Chapter 3. --- Dynamic Cell Sectoring --- p.19Chapter 3.1. --- Introduction --- p.19Chapter 3.2. --- Minimum Total Transmission Power sectoring --- p.21Chapter 3.2.1. --- Problem Statement --- p.21Chapter 3.2.2. --- Shortest Path Problem Formulation --- p.23Chapter 3.2.3. --- Shortest Path Algorithm and Complexity --- p.26Chapter 3.2.4. --- Graph Reduction --- p.28Chapter 3.2.5. --- Example --- p.30Chapter 3.3. --- Power Equalization Sectoring --- p.33Chapter 3.3.1. --- Relationship Between MinTTP Sectoring and PE Sectoring --- p.33Chapter 3.3.2. --- Power Equalization Sectoring Algorithm --- p.36Chapter 3.4. --- Numerical Results --- p.37Appendix --- p.44Chapter Chapter 4. --- Resectoring Algorithms --- p.46Chapter 4.1. --- Introduction --- p.46Chapter 4.2. --- Nyquist Sampling Theorem --- p.47Chapter 4.3. --- MinTTP Resectoring --- p.47Chapter 4.4. --- PE Resectoring --- p.43Chapter 4.5. --- Handoff --- p.48Chapter 4.5.1. --- Handoff Load --- p.49Chapter 4.6. --- Performance --- p.49Chapter Chapter 5. --- Conclusion and Future Work --- p.53Chapter 5.1. --- Thesis Summary --- p.53Chapter 5.2. --- Future Work --- p.54Bibliography --- p.5

Intégration de la blockchain à l'Internet des objets

L'Internet des objets (IdO) est en train de transformer l'industrie traditionnelle en une industrie intelligente où les décisions sont prises en fonction des données. L'IdO interconnecte de nombreux objets (ou dispositifs) qui effectuent des tâches complexes (e.g., la collecte de données, l'optimisation des services, la transmission de données). Toutefois, les caractéristiques intrinsèques de l'IdO entraînent plusieurs problèmes, tels que la décentralisation, une faible interopérabilité, des problèmes de confidentialité et des failles de sécurité. Avec l'évolution attendue de l'IdO dans les années à venir, il est nécessaire d'assurer la confiance dans cette énorme source d'informations entrantes. La blockchain est apparue comme une technologie clé pour relever les défis de l'IdO. En raison de ses caractéristiques saillantes telles que la décentralisation, l'immuabilité, la sécurité et l'auditabilité, la blockchain a été proposée pour établir la confiance dans plusieurs applications, y compris l'IdO. L'intégration de la blockchain a l'IdO ouvre la porte à de nouvelles possibilités qui améliorent intrinsèquement la fiabilité, la réputation, et la transparence pour toutes les parties concernées, tout en permettant la sécurité. Cependant, les blockchains classiques sont coûteuses en calcul, ont une évolutivité limitée, et nécessitent une bande passante élevée, ce qui les rend inadaptées aux environnements IdO à ressources limitées. L'objectif principal de cette thèse est d'utiliser la blockchain comme un outil clé pour améliorer l'IdO. Pour atteindre notre objectif, nous relevons les défis de la fiabilité des données et de la sécurité de l'IdO en utilisant la blockchain ainsi que de nouvelles technologies émergentes, notamment l'intelligence artificielle (IA). Dans la première partie de cette thèse, nous concevons une blockchain qui garantit la fiabilité des données, adaptée à l'IdO. Tout d'abord, nous proposons une architecture blockchain légère qui réalise la décentralisation en formant un réseau superposé où les dispositifs à ressources élevées gèrent conjointement la blockchain. Ensuite, nous présentons un algorithme de consensus léger qui réduit la puissance de calcul, la capacité de stockage, et la latence de la blockchain. Dans la deuxième partie de cette thèse, nous concevons un cadre sécurisé pour l'IdO tirant parti de la blockchain. Le nombre croissant d'attaques sur les réseaux IdO, et leurs graves effets, rendent nécessaire la création d'un IdO avec une sécurité plus sophistiquée. Par conséquent, nous tirons parti des modèles IA pour fournir une intelligence intégrée dans les dispositifs et les réseaux IdO afin de prédire et d'identifier les menaces et les vulnérabilités de sécurité. Nous proposons un système de détection d'intrusion par IA qui peut détecter les comportements malveillants et contribuer à renforcer la sécurité de l'IdO basé sur la blockchain. Ensuite, nous concevons un mécanisme de confiance distribué basé sur des contrats intelligents de blockchain pour inciter les dispositifs IdO à se comporter de manière fiable. Les systèmes IdO existants basés sur la blockchain souffrent d'une bande passante de communication et d’une évolutivité limitée. Par conséquent, dans la troisième partie de cette thèse, nous proposons un apprentissage machine évolutif basé sur la blockchain pour l'IdO. Tout d'abord, nous proposons un cadre IA multi-tâches qui exploite la blockchain pour permettre l'apprentissage parallèle de modèles. Ensuite, nous concevons une technique de partitionnement de la blockchain pour améliorer l'évolutivité de la blockchain. Enfin, nous proposons un algorithme d'ordonnancement des dispositifs pour optimiser l'utilisation des ressources, en particulier la bande passante de communication.Abstract : The Internet of Things (IoT) is reshaping the incumbent industry into a smart industry featured with data-driven decision making. The IoT interconnects many objects (or devices) that perform complex tasks (e.g., data collection, service optimization, data transmission). However, intrinsic features of IoT result in several challenges, such as decentralization, poor interoperability, privacy issues, and security vulnerabilities. With the expected evolution of IoT in the coming years, there is a need to ensure trust in this huge source of incoming information. Blockchain has emerged as a key technology to address the challenges of IoT. Due to its salient features such as decentralization, immutability, security, and auditability, blockchain has been proposed to establish trust in several applications, including IoT. The integration of IoT and blockchain opens the door for new possibilities that inherently improve trustworthiness, reputation, and transparency for all involved parties, while enabling security. However, conventional blockchains are computationally expensive, have limited scalability, and incur significant bandwidth, making them unsuitable for resource-constrained IoT environments. The main objective of this thesis is to leverage blockchain as a key enabler to improve the IoT. Toward our objective, we address the challenges of data reliability and IoT security using the blockchain and new emerging technologies, including machine learning (ML). In the first part of this thesis, we design a blockchain that guarantees data reliability, suitable for IoT. First, we propose a lightweight blockchain architecture that achieves decentralization by forming an overlay network where high-resource devices jointly manage the blockchain. Then, we present a lightweight consensus algorithm that reduces blockchain computational power, storage capability, and latency. In the second part of this thesis, we design a secure framework for IoT leveraging blockchain. The increasing number of attacks on IoT networks, and their serious effects, make it necessary to create an IoT with more sophisticated security. Therefore, we leverage ML models to provide embedded intelligence in the IoT devices and networks to predict and identify security threats and vulnerabilities. We propose a ML intrusion detection system that can detect malicious behaviors and help further bolster the blockchain-based IoT’s security. Then, we design a distributed trust mechanism based on blockchain smart contracts to incite IoT devices to behave reliably. Existing blockchain-based IoT systems suffer from limited communication bandwidth and scalability. Therefore, in the third part of this thesis, we propose a scalable blockchain-based ML for IoT. First, we propose a multi-task ML framework that leverages the blockchain to enable parallel model learning. Then, we design a blockchain partitioning technique to improve the blockchain scalability. Finally, we propose a device scheduling algorithm to optimize resource utilization, in particular communication bandwidth