WSN低能耗数据收集遗传粒子群算法研究

doi:10.11896/j.issn.1002-137X.2017.03.019

Abstract

Abstract: Aiming at the problems in green house wireless sensor networks such as uneven nodes distribution,strict energy constraint,etc,an improved genetic particle swarm optimization algorithm was proposed to solve the problem of total energy consumption in data collection of wireless sensor networks.This algorithm uses the parent node representation method to encode the spanning tree into particles.An algorithm for generating random data collection tree was designed,which can satisfy the spanning tree of tree height.A single point mutation algorithm was designed,which makes the spanning tree satisfy the height limit of the tree.The particles get the next iteration by the single point mutation,the extreme value of the individual and the global extreme value time.Under the same number of hops,the simulation results show that the algorithm proposed in this paper reduces 7.34% of the total energy consumption compared with the DL-DCT and it prolongs the average lifetime of network.

Key words: Wireless sensor network,Data gathering,Genetic algorithm,Particle swarm optimization,Low energy consumption

WANG Hong-lei, XU Ping-ping, ZHU Wen-xiang and YOU Xing-miao. Research on Low Energy-consumption Data Collection for WSN Environment Based on Genetic Particle Swarm Optimization[J].Computer Science, 2017, 44(3): 79-83.

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