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

被引：0
作者：
Sun A. [1 ]
Li S. [1 ]
Zhang Y. [2 ]
机构：
[1] School of Communication and Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an
[2] School of Electronic Engineering, Xi'an University of Posts & Telecommunications, Xi'an
来源：
Tongxin Xuebao/Journal on Communications | 2021年 / 42卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Cat swarm optimization; Fuzzy C-means; Load balancing; Particle swarm optimization; Wireless sensor network;
D O I：
10.11959/j.issn.1000-436x.2021053
中图分类号：
学科分类号：
摘要：
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. © 2021, Editorial Board of Journal on Communications. All right reserved.
引用
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页码：91 / 99
页数：8
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