基于PSO优化模糊C均值的WSN分簇路由算法

doi:10.11959/j.issn.1000-436x.2021053

Abstract

Abstract:

Aimed at the problems of limited energy and unbalanced load in wireless sensor network, POFCA based on particle swarm optimization fuzzy C-means was proposed.POFCA was respectively optimized from the cluster stage and the data transmission stage.In the clustering stage, the particle swarm optimization fuzzy C-means was firstly used to overcome the sensitivity to the initial clustering center.And the cluster head was dynamically updated according to the remaining power and the relative distance of the nodes to balance the network load.Then in the data transfer phase, a path evaluation function was designed based on the distance factor, the energy factor and the nodal load.Besides, the cat swarm optimization was used to search the optimal routing path for the cluster head to balance the load of the cluster head without increasing the load of the relay node.The simulation result shows that compared with algorithms of LEACH and LEACH-improved, POFCA can effectively balance the network load, reduce the energy consumption of nodes and extend the lifetime of the entire network.

Key words: wireless sensor network, particle swarm optimization, fuzzy C-means, cat swarm optimization, load balancing

CLC Number:

TP393

Aijing SUN, Shichang LI, Yicai ZHANG. WSN clustering routing algorithm based on PSO optimized fuzzy C-means[J]. Journal on Communications, 2021, 42(3): 91-99.

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参数	取值
节点数目	100
基站位置	(50,50)
监测范围	100 m×100 m
数据分组长/bit	4 000
控制分组长/bit	100
粒子数	20
网络系数λ	0.7
模糊因子m	2
最优传输距离d_best/m	32
SMP记忆池大小	5
惯性权重w	[0.2,0.9]
E_init /J	0.5
E_elec/(nJ?bit^?1)	50
E_fs/(pJ?(bit?m²)^?1)	10
E_mp/(pJ?(bit?m⁴)^?1)	0.001 3

WSN clustering routing algorithm based on PSO optimized fuzzy C-means

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