Channel access framework for cognitive radio-based wireless sensor networks using reinforcement learning

Abstract

Cognitive radio-based wireless sensor network is a new paradigm in sensor networks research. It is considered to revolutionize next generation sensor networks. Therefore, it is of paramount importance to develop an efficient channel access technique suitable for cognitive radio-based wireless sensor network. In this paper we have proposed a channel access framework for cognitive radio-based wireless sensor networks which is based on reinforcement learning technique. We have used Q-learning approach to develop a simple access algorithm. We have analyzed the effect of sensing time on the probability of detection, probability of misdetection and probability of false alarm. These parameters were compared using different detection threshold values and significant simulation results were discussed.