Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

SPECTRUM SENSING AND COOPERATION IN COGNITIVE-OFDM BASED WIRELESS COMMUNICATIONS NETWORKS

The world has witnessed the development of many wireless systems and applications. In addition to the large number of existing devices, such development of new and advanced wireless systems increases rapidly the demand for more radio spectrum. The radio spectrum is a limited natural resource; however, it has been observed that it is not efficiently utilized. Consequently, different dynamic spectrum access techniques have been proposed as solutions for such an inefficient use of the spectrum. Cognitive Radio (CR) is a promising intelligent technology that can identify the unoccupied portions of spectrum and opportunistically uses those portions with satisfyingly high capacity and low interference to the primary users (i.e., licensed users). The CR can be distinguished from the classical radio systems mainly by its awareness about its surrounding radio frequency environment. The spectrum sensing task is the main key for such awareness. Due to many advantages, Orthogonal Frequency Division Multiplexing system (OFDM) has been proposed as a potential candidate for the CR‟s physical layer. Additionally, the Fast Fourier Transform (FFT) in an OFDM receiver supports the performance of a wide band spectrum analysis. Multitaper spectrum estimation method (MTM) is a non-coherent promising spectrum sensing technique. It tolerates problems related to bad biasing and large variance of power estimates. This thesis focuses, generally, on the local, multi antenna based, and global cooperative spectrum sensing techniques at physical layer in OFDM-based CR systems. It starts with an investigation on the performance of using MTM and MTM with singular value decomposition in CR networks using simulation. The Optimal MTM parameters are then found. The optimal MTM based detector theoretical formulae are derived. Different optimal and suboptimal multi antenna based spectrum sensing techniques are proposed to improve the local spectrum sensing performance. Finally, a new concept of cooperative spectrum sensing is introduced, and new strategies are proposed to optimize the hard cooperative spectrum sensing in CR networks. The MTM performance is controlled by the half time bandwidth product and number of tapers. In this thesis, such parameters have been optimized using Monte Carlo simulation. The binary hypothesis test, here, is developed to ensure that the effect of choosing optimum MTM parameters is based upon performance evaluation. The results show how these optimal parameters give the highest performance with minimum complexity when MTM is used locally at CR. The optimal MTM based detector has been derived using Neyman-Pearson criterion. That includes probabilities of detection, false alarm and misses detection approximate derivations in different wireless environments. The threshold and number of sensed samples controlling is based on this theoretical work. In order to improve the local spectrum sensing performance at each CR, in the CR network, multi antenna spectrum sensing techniques are proposed using MTM and MTM with singular value decomposition in this thesis. The statistical theoretical formulae of the proposed techniques are derived including the different probabilities. ii The proposed techniques include optimal, that requires prior information about the primary user signal, and two suboptimal multi antenna spectrum sensing techniques having similar performances with different computation complexity; these do not need prior information about the primary user signalling. The work here includes derivations for the periodogram multi antenna case. Finally, in hard cooperative spectrum sensing, the cooperation optimization is necessary to improve the overall performance, and/or minimize the number of data to be sent to the main CR-base station. In this thesis, a new optimization method based on optimizing the number of locally sensed samples at each CR is proposed with two different strategies. Furthermore, the different factors that affect the hard cooperative spectrum sensing optimization are investigated and analysed and a new cooperation scheme in spectrum sensing, the master node, is proposed.Ministry of Interior-Kingdom of Saudi Arabi

Distributed Detection and Estimation in Wireless Sensor Networks

In this article we consider the problems of distributed detection and estimation in wireless sensor networks. In the first part, we provide a general framework aimed to show how an efficient design of a sensor network requires a joint organization of in-network processing and communication. Then, we recall the basic features of consensus algorithm, which is a basic tool to reach globally optimal decisions through a distributed approach. The main part of the paper starts addressing the distributed estimation problem. We show first an entirely decentralized approach, where observations and estimations are performed without the intervention of a fusion center. Then, we consider the case where the estimation is performed at a fusion center, showing how to allocate quantization bits and transmit powers in the links between the nodes and the fusion center, in order to accommodate the requirement on the maximum estimation variance, under a constraint on the global transmit power. We extend the approach to the detection problem. Also in this case, we consider the distributed approach, where every node can achieve a globally optimal decision, and the case where the decision is taken at a central node. In the latter case, we show how to allocate coding bits and transmit power in order to maximize the detection probability, under constraints on the false alarm rate and the global transmit power. Then, we generalize consensus algorithms illustrating a distributed procedure that converges to the projection of the observation vector onto a signal subspace. We then address the issue of energy consumption in sensor networks, thus showing how to optimize the network topology in order to minimize the energy necessary to achieve a global consensus. Finally, we address the problem of matching the topology of the network to the graph describing the statistical dependencies among the observed variables.Comment: 92 pages, 24 figures. To appear in E-Reference Signal Processing, R. Chellapa and S. Theodoridis, Eds., Elsevier, 201

Decentralized Detection With Correlated Gaussian Observations: Parallel And Tandem Networks With Two Sensors

Signal detection in cognitive radio involves the determination of presence or absence of a primary user signal so that the secondary user may opportunistically gain access when the spectrum is unoccupied. In decentralized sensing scheme, two or more secondary users sense the spectrum, process individual observation and then pass the quantized data to a fusion center where a decision with regard to which hypothesis being true, that is, a signal being present or absent, is made. In the second part of the thesis, we study Bayes error performance of two-sensor tandem network designed to detect the presence or absence of deterministic signals in correlated Gaussian noise. Hence, the correlation coefficient remains identical under both hypotheses. Specifically, we address the question of which sensor ought to serve as the fusion center for optimal detection performance. In the process of this query, we draw some inference parallel to the “Good, Bad and Ugly’’ signal regions formulated originally for the two-sensor one-bit-per-sensor parallel fusion network by Willet,et.al. In the tandem “Good” region, numerical results conclusively show that the strategy of placing better sensor, i.e the sensor with higher signal to noise ratio, serving as the fusion center is preferred for better detection performance. In the first part of thesis, we study the error performance in a parallel network consisting of two sensors. In the parallel configuration, each sensor quantizes it\u27s own observation into a single-bit and transmits them to the fusion center. At the fusion center, the performance of AND and OR rules are examined by assuming the observations at the two sensors are jointly Gaussian, with specific means, variances and correlation coefficient, under hypothesis H1, whereas the observations under H0 are still Gaussian with specific means and variances but are statistically independent. The optimum quantizers at each sensor are found by minimizing the probability of error at the fusion center. We use a genetic algorithm (GA) to find a sub-optimal solution. It was observed that, when prior probabilities of hypotheses are equal, AND performs at least as well as OR

On Uniformly Most Powerful Decentralized Detection

The theory behind Uniformly Most Powerful (UMP) composite binary hypothesis testing is mature and well defined in centralized detection where all observations are directly accessible at one central node. However, within the area of decentralized detection, UMP tests have not been researched, even though tests of this nature have properties that are highly desirable. The purpose of this research is to extend the UMP concept into decentralized detection, which we define as UMP decentralized detection (UMP-DD). First, the standard parallel decentralized detection model with conditionally independent observations will be explored. This section will introduce theorems and corollaries that define when UMP-DD exists and provide counterintuitive examples where UMP-DD tests do not exist. Second, we explore UMP-DD for directed single-rooted trees of bounded height. We will show that a binary relay tree achieves a Type II error probability exponent that is equivalent to the parallel structure even if all the observations are not identically distributed. We then show that the optimal configuration can also achieve UMP-DD performance, while the tandem configuration does not achieve UMP-DD performance. Finally, we relax the assumption of conditional independence and show under specific constraints that both the parallel and binary relay tree configurations can still be UMP-DD. Throughout, examples will be provided that tie this theoretical work together with current research in fields such as Cognitive Radio