Allocation of multi-objective cross-layer wireless sensor network resource based on chaotic parallel genetic algorithm

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.