Abstract
High density wireless sensor networks (HDWSNs) are emerging as promising techniques in a variety of fields such as target detection and tracking, military surveillance, intelligent family, preventing forest fire loss, building monitoring and control, medical diagnostic, etc. HDWSNs composed of a large number of sensors with wireless communication, computation, information acquisition, and self-adaptation abilities. Maximizing the rate of monitored targets is important in enhancing effect of detection in HDWSNs. Target scheduling is a technique providing a method that shows how to maximizing the rate of monitored targets. Since the computational complexity of target scheduling increases exponentially when the number of sensors nodes increases, traditional mathematical methods is less effective for this combinatorial optimization problem in large HDWSNs. In this paper, a chaotic elite clone evolutionary algorithm (CECEA) is investigated to explore the search space with a small group of individuals. A fitness function for evaluating the rate of monitored targets is also designed. The proposed CECEA combines the merits of chaotic generator and elite operator to give a more efficient evolution process. Simulations are conducted using CECEA, and the results are comparatively evaluated against the parallel genetic algorithm (PGA) and particle swarm optimization (PSO). Simulation results show that the proposed CECEA is effective and can greatly enhance the rate of monitored targets than PGA and PSO for the target scheduling in HDWSNs.
Published Version
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