Fast Channel Selection Strategy in Cognitive Wireless Sensor Networks

Abstract

In order to meet the practical requirement for Cognitive Wireless Sensor Networks applications, this paper proposes innovative fast channel selection algorithm to solve the shortcomings of original Experience-Weighted Attraction algorithm's complexity, higher energy consuming, and the nodes’ hardware restrictions of real-time data processing capabilities. Research is conducted by comparing channel selection differences and timeliness with traditional Experience-Weighted Attraction learning. Though not as stable as traditional Experience-Weighted Attraction learning, fast channel selection algorithm has effectively reduced the complexity of the original algorithm and has superior performance than Q learning.