Bioinformatics-Inspired Quantized Hard Combination-Based Abnormality Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks

Abstract

String matching algorithms used in bioinformatics can be applied to scenarios in cognitive radios, where reports of cooperative spectrum sensing nodes need to be compared with each other. Cooperative spectrum sensing is susceptible to security risks, where malicious users who participate in the process falsify the spectrum sensing data, thus affecting cognitive radio network performance. In this paper, an efficient spectrum sensing system is developed where each cognitive radio (CR) user senses the spectrum multiple times within an allocated sensing period. Each CR user quantizes its decision to predefined levels so as to achieve a tradeoff between bandwidth utilization and decision reporting accuracy. The reports for all the CR users are compared at the fusion center using Smith–Waterman algorithm, an optimal algorithm for aligning biological sequences used in bioinformatics, and similarity indices are computed. Robust mean and robust deviation of the similarity indices are calculated and a threshold is determined by these values. The CR users who have similarity indices below the given threshold are declared malicious and their reports are discarded. The local decisions of the remaining CR users are combined using the modified rules of decision combination to take a global decision. Simulation results show that our proposed scheme performs better than conventional schemes with and without malicious users.