WSN低能耗数据收集遗传粒子群算法研究

针对设施农业无线传感器网络节点分布不均匀、能量约束严格的特点,为降低网络总能耗,提出一种改进的遗传粒子群算法,构建一棵树高受限且网络总能耗最小的数据收集树。首先,随机生成连通图网络,采用父节点表示法将生成树编码成粒子；然后,设计一种随机生成数据收集树算法,随机产生满足树高限制的生成树；最后,考虑节点能耗均衡,设计一种粒子单点突变算法,实现对节点能耗最优值的比较。通过粒子单点变异、交叉以及优化新粒子,提高了种群多样性,避免了算法过早陷入局部最优解,在满足时延要求的同时,降低了网络总能耗。实验表明,与有树高约束的DL-DCT算法相比,所提算法降低了7.34%的网络总能耗,延长了网络平均生存期。

Research on Low Energy-consumption Data Collection for WSN Environment Based on Genetic Particle Swarm Optimization

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.