Intelligent Approaches for Energy-Efficient Resource Allocation in the Cognitive Radio Network

The cognitive radio (CR) is evolved as the promising technology to alleviate the spectrum scarcity issues by allowing the secondary users (SUs) to use the licensed band in an opportunistic manner. Various challenges need to be addressed before the successful deployment of CR technology. This thesis work presents intelligent resource allocation techniques for improving energy efficiency (EE) of low battery powered CR nodes where resources refer to certain important parameters that directly or indirectly affect EE. As far as the primary user (PU) is concerned, the SUs are allowed to transmit on the licensed band until their transmission power would not cause any interference to the primary network. Also, the SUs must use the licensed band efficiently during the PU’s absence. Therefore, the two key factors such as protection to the primary network and throughput above the threshold are important from the PU’s and SUs’ perspective, respectively. In deployment of CR, malicious users may be more active to prevent the CR users from accessing the spectrum or cause unnecessary interference to the both primary and secondary transmission. Considering these aspects, this thesis focuses on developing novel approaches for energy-efficient resource allocation under the constraints of interference to the PR, minimum achievable data rate and maximum transmission power by optimizing the resource parameters such as sensing time and the secondary transmission power with suitably selecting SUs. Two different domains considered in this thesis are the soft decision fusion (SDF)-based cooperative spectrum sensing CR network (CRN) models without and with the primary user emulation attack (PUEA). An efficient iterative algorithm called iterative Dinkelbach method (IDM) is proposed to maximize EE with suitable SUs in the absence of the attacker. In the proposed approaches, different constraints are evaluated considering the negative impact of the PUE attacker on the secondary transmission while maximizing EE with the PUE attacker. The optimization problem associated with the non-convex constraints is solved by our proposed iterative resource allocation algorithms (novel iterative resource allocation (NIRA) and novel adaptive resource allocation (NARA)) with suitable selection of SUs for jointly optimizing the sensing time and power allocation. In the CR enhanced vehicular ad hoc network (CR-VANET), the time varying channel responses with the vehicular movement are considered without and with the attacker. In the absence of the PUE attacker, an interference-aware power allocation scheme based on normalized least mean square (NLMS) algorithm is proposed to maximize EE considering the dynamic constraints. In the presence of the attacker, the optimization problem associated with the non-convex and time-varying constraints is solved by an efficient approach based on genetic algorithm (GA). Further, an investigation is attempted to apply the CR technology in industrial, scientific and medical (ISM) band through spectrum occupancy prediction, sub-band selection and optimal power allocation to the CR users using the real time indoor measurement data. Efficacies of the proposed approaches are verified through extensive simulation studies in the MATLAB environment and by comparing with the existing literature. Further, the impacts of different network parameters on the system performance are analyzed in detail. The proposed approaches will be highly helpful in designing energy-efficient CRN model with low complexity for future CR deployment

Cooperative-hybrid detection of primary user emulators in cognitive radio networks

Primary user emulator (PUE) attack occurs in Cognitive Radio Networks (CRNs) when a malicious secondary user (SU) poses as a primary user (PU) in order to deprive other legitimate SUs the right to free spectral access for opportunistic communication. In most cases, these legitimate SUs are unable to effectively detect PUEs because the quality of the signals received from a PUE may be severely attenuated by channel fading and/or shadowing. Consequently, in this paper, we have investigated the use of cooperative spectrum sensing (CSS) to improve PUE detection based on a hybrid localization scheme. We considered different pairs of secondary users (SUs) over different received signal strength (RSS) values to evaluate the energy efficiency, accuracy, and speed of the new cooperative scheme. Based on computer simulations, our findings suggest that a PUE can be effectively detected by a pair of SUs with a low Root Mean Square Error rate of 0.0047 even though these SUs may have close RSS values within the same cluster. Furthermore, our scheme performs better in terms of speed, accuracy and low energy consumption rates when compared with other PUE detection schemes. Thus, it is a viable proposition to better detect PUEs in CRNs

Security Supports for Cyber-Physical System and its Communication Networks

A cyber-physical system (CPS) is a sensing and communication platform that features tight integration and combination of computation, networking, and physical processes. In such a system, embedded computers and networks monitor and control the physical processes through a feedback loop, in which physical processes affect computations and vice versa. In recent years, CPS has caught much attention in many different aspects of research, such as security and privacy. In this dissertation, we focus on supporting security in CPS and its communication networks. First, we investigate the electric power system, which is an important CPS in modern society. as crucial and valuable infrastructure, the electric power system inevitably becomes the target of malicious users and attackers. In our work, we point out that the electric power system is vulnerable to potential cyber attacks, and we introduce a new type of attack model, in which an attack cannot be completely identified, even though its presence may be detected. to defend against such an attack, we present an efficient heuristic algorithm to narrow down the attack region, and then enumerate all feasible attack scenarios. Furthermore, based on the feasible attack scenarios, we design an optimization strategy to minimize the damage caused by the attack. Next, we study cognitive radio networks, which are a typical communication network in CPS in the areas of security and privacy. as for the security of cognitive radio networks, we point out that a prominent existing algorithm in cooperative spectrum sensing works poorly under a certain attack model. In defense of this attack, we present a modified combinatorial optimization algorithm that utilizes the branch-and-bound method in a decision tree to identify all possible false data efficiently. In regard to privacy in cognitive radio networks, we consider incentive-based cognitive radio transactions, where the primary users sell time slices of their licensed spectrum to secondary users in the network. There are two concerns in such a transaction. The first is the primary user\u27s interest, and the second is the secondary user\u27s privacy. to verify that the payment made by a secondary user is trustworthy, the primary user needs detailed spectrum utilization information from the secondary user. However, disclosing this detailed information compromises the secondary user\u27s privacy. to solve this dilemma, we propose a privacy-preserving scheme by repeatedly using a commitment scheme and zero-knowledge proof scheme

Spectrum Sensing Security in Cognitive Radio Networks

This thesis explores the use of unsupervised machine learning for spectrum sensing in cognitive radio (CR) networks from a security perspective. CR is an enabling technology for dynamic spectrum access (DSA) because of a CR's ability to reconfigure itself in a smart way. CR can adapt and use unoccupied spectrum with the help of spectrum sensing and DSA. DSA is an efficient way to dynamically allocate white spaces (unutilized spectrum) to other CR users in order to tackle the spectrum scarcity problem and improve spectral efficiency. So far various techniques have been developed to efficiently detect and classify signals in a DSA environment. Neural network techniques, especially those using unsupervised learning have some key advantages over other methods mainly because of the fact that minimal preconfiguration is required to sense the spectrum. However, recent results have shown some possible security vulnerabilities, which can be exploited by adversarial users to gain unrestricted access to spectrum by fooling signal classifiers. It is very important to address these new classes of security threats and challenges in order to make CR a long-term commercially viable concept. This thesis identifies some key security vulnerabilities when unsupervised machine learning is used for spectrum sensing and also proposes mitigation techniques to counter the security threats. The simulation work demonstrates the ability of malicious user to manipulate signals in such a way to confuse signal classifier. The signal classifier is forced by the malicious user to draw incorrect decision boundaries by presenting signal features which are akin to a primary user. Hence, a malicious user is able to classify itself as a primary user and thus gains unrivaled access to the spectrum. First, performance of various classification algorithms are evaluated. K-means and weighted classification algorithms are selected because of their robustness against proposed attacks as compared to other classification algorithm. Second, connection attack, point cluster attack, and random noise attack are shown to have an adverse effect on classification algorithms. In the end, some mitigation techniques are proposed to counter the effect of these attacks

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig