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基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配
Allocation of multi-objective cross-layer wireless sensor network resource based on chaotic parallel genetic algorithm
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摘要: 提出了一种基于混沌并行遗传算法的多目标无线传感器网络跨层资源分配方法,该方法运用混沌序列和并行遗传算法来动态调整传感器网络节点的探测目标及通信时隙等参数,对资源分配方式进行跨层整体优化.在多目标无线传感器网络环境下,将本文方法与传统的随机分配方法、动态规划方法、T-MAC协议及S-MAC协议等资源分配算法进行了仿真比较.仿真结果表明,本文提出的混沌并行遗传算法具有通信时延小,目标检测成功率高等优点,在降低了无线传感器网络功率消耗的同时提高了对目标检测的实时性.Abstract: A chaotic parallel genetic algorithm for the allocation of a multi-objective cross-layer wireless sensor network resource is provided, in which chaotic sequence and parallel genetic algorithm are used to dynamically adjust target selection, communication time slots and other parameters for optimizing the global cross-layer resource allocation. Simulations are conducted to compare the chaotic parallel genetic algorithm method with random allocation algorithm, dynamic programming algorithm, T-MAC protocol and the S-MAC protocol separalely. The simulation results show that the chaotic parallel genetic algorithm has a small communication delay and high success rate of target detection, which reduces the power consumption and improves the real-time characteristic of wireless sensor network.
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