Deep learning systems as complex networks

Thanks to the availability of large scale digital datasets and massive amounts of computational power, deep learning algorithms can learn representations of data by exploiting multiple levels of abstraction. These machine learning methods have greatly improved the state-of-the-art in many challenging cognitive tasks, such as visual object recognition, speech processing, natural language understanding and automatic translation. In particular, one class of deep learning models, known as deep belief networks, can discover intricate statistical structure in large data sets in a completely unsupervised fashion, by learning a generative model of the data using Hebbian-like learning mechanisms. Although these self-organizing systems can be conveniently formalized within the framework of statistical mechanics, their internal functioning remains opaque, because their emergent dynamics cannot be solved analytically. In this article we propose to study deep belief networks using techniques commonly employed in the study of complex networks, in order to gain some insights into the structural and functional properties of the computational graph resulting from the learning process.Comment: 20 pages, 9 figure

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks

Dynamic frequency planning for professional wireless microphone systems

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Secure mobile edge server placement using multi-agent reinforcement learning

Funding Information: Funding: This work is supported by King Khaled University under Grant Agreement No. 6204.Peer reviewedPublisher PD

Discrete and Continuous Optimization Methods for Self-Organization in Small Cell Networks - Models and Algorithms

Self-organization is discussed in terms of distributed computational methods and algorithms for resource allocation in cellular networks. In order to develop algorithms for different self-organization problems pertinent to small cell networks (SCN), a number of concepts from discrete and continuous optimization theory are employed. Self-organized resource allocation problems such as physical cell identifier (PCI) assignment and primary component carrier selection are formulated as discrete optimization problems. Distributed graph coloring and constraint satisfaction algorithms are used to solve these problems. The PCI assignment is also discussed for multi-operator heterogeneous networks. Furthermore, different variants of simulated annealing are proposed for solving a graph coloring formulation of the orthogonal resource allocation problem. In the continuous optimization domain, a network utility maximization approach is considered for solving different resource allocation problems. Network synchronization is addressed using greedy and gradient search algorithms. Primal and dual decomposition are discussed for transmit power and scheduling weight optimizations, under a network-wide power constraint. Joint optimization over transmit powers and multi-user scheduling weights is considered in a multi-carrier SCN, for both maximum rate and proportional-fair rate utilities. This formulation is extended for multiple-input multiple-output (MIMO) SCNs, where apart from transmit powers and multi-user scheduling weights, the transmit precoders are also optimized, for a generic alpha-fair utility function. Optimization of network resources over multiple degrees of freedom is particularly effective in reducing mutual interference, leading to significant gains in network utility. Finally, an alternate formulation of transmit power allocation is considered, in which the network transmit power is minimized subject to the data rate constraints of users. Thus, network resource allocation algorithms inspired by optimization theory constitute an effective approach for self-organization in contemporary as well as future cellular networks

Navigation coopérative de véhicules autonomes basée sur la communication V2X dans un réseau de 5ème génération

In today’s world, road transport is essential to our daily routines and business activities. However, the exponential growth in the number of vehicles has led to problems such as traffic congestion and road accidents. Vehicular communication presents an innovative solution, envisaging a future where vehicles communicate with each other, the road infrastructure, and even the road itself, sharing real-time data to optimize traffic flow and enhance safety. This thesis focuses on 5G and Beyond 5G (B5G) technologies, which promise to revolutionize Vehicle-to-Everything (V2X) communication. With the emergence of millimeter-wave (mmWave) communication, high-speed, low-latency data transmission is essential for vehicular networks. However, mmWave communication faces problems with signal attenuation and interference. Our research focuses on solving these problems using a deep learning-based approach. Three significant contributions are proposed. First, we introduce a classical optimization technique, the simulated annealing algorithm, to improve beam alignment in 5G vehicular networks. This reduces latency and improves data transmission between millimeter-wave base stations and vehicles. Our second contribution is a new approach involving a hybrid deep-learning model that predicts optimal beam angles. Combining a 1D CNN and a BiLSTM improves th accuracy of the prediction and reduces errors. This approach eliminates time-consuming computations and iterations critical to the success of B5G vehicular networks. The third contribution introduces a BiLSTM-based model to select the optimal beam pair angles at the mmWave base station (mmBS) and the moving vehicle side. This approach improves the reliability of data transmission while minimizing the error probabilities and overheads during beam search. This research contributes to advancing vehicular communications, offering innovative solutions for 5G and B5G networks. We aim to enhance the efficiency, reduce the latency, and improve the reliability of communications for connected vehicles. This thesis explores beam alignment through classical and deep learning techniques and presents solutions for the challenges of millimeter-wave vehicular networks. Our research provides the foundation for the next generation of vehicular communication and its vital role in making road transport safer and more efficient

Multiple Objective Fitness Functions for Cognitive Radio Adaptation

This thesis explores genetic algorithm and rule-based optimization techniques used by cognitive radios to make operating parameter decisions. Cognitive radios take advantage of intelligent control methods by using sensed information to determine the optimal set of transmission parameters for a given situation. We have chosen to explore and compare two control methods. A biologically-inspired genetic algorithm (GA) and a rule-based expert system are proposed, analyzed and tested using simulations. We define a common set of eight transmission parameters and six environment parameters used by cognitive radios, and develop a set of preliminary fitness functions that encompass the relationships between a small set of these input and output parameters. Five primary communication objectives are also defined and used in conjunction with the fitness functions to direct the cognitive radio to a solution. These fitness functions are used to implement the two cognitive control methods selected. The hardware resources needed to practically implement each technique are studied. It is observed, through simulations, that several trade offs exist between both the accuracy and speed of the final decision and the size of the parameter sets used to determine the decision. Sensitivity analysis is done on each parameter in order to determine the impact on the decision making process each parameter has on the cognitive engine. This analysis quantifies the usefulness of each parameter