Analysis of a New Energy-Based Sensor Selection Method for Cooperative Spectrum Sensing in Cognitive Radio Networks

Abstract

Spectrum sensing is essential to identify the presence of primary users (PUs) in cognitive radio networks. Cooperative spectrum sensing (CSS) obtained from the diversity of different sensors improves the reliability of decisions made about the presence of PU. Although CSS offers significant advantages, it may introduce some shortcomings including the energy consumption and overhead in the sensing phase. Also, due to the limited life span of batteries, the energy consumption process for performing CSS should be balanced among sensors so that some sensors do not encounter rapid battery drain. Therefore, the management of sensors for the participation in CSS is of great importance. In this paper, a new energy-based sensor selection algorithm is proposed to provide approximately the same lifetimes for sensors via the appropriate design of CSS. Furthermore, a mathematical model is proposed to analyze the process of energy consumption for sensors. The system model we consider in this paper focuses on different sensors consuming various amounts of energy for CSS. The analysis and simulation results show that the proposed algorithm achieves approximate fairness among sensors in terms of lifetime. In addition, comparison between the proposed algorithm and the existing algorithms reveals that our proposed algorithm can balance the process of energy consumption among sensors and significantly increase the average number of live sensors before the network lifetime finishes.