摘要
针对无线传感器网络(Wireless Sensor Networks,WSN)节点能耗不均衡问题,提出一种基于改进人工鱼群算法(Improved Artificial Fish Swarm Algorithm,IAFSA)和模糊C均值(Fuzzy C-Means,FCM)的分簇算法IAFCA。首先,IAFSA改进搜素视野及步长,避免迭代解陷入局部最优解,并将最终的迭代解作为FCM的初始聚类中心,克服FCM对初始解的敏感性。其次,FCM在确定最佳簇头数目的基础上,根据节点间的距离相似性,有效建立起节点与聚类中心间的不确定性关系,合理进行节点分簇。最后,根据节点相对剩余能量和到聚类中心的距离两个参数选举出簇头。IAFCA分别在两种场景下进行仿真实验,并与低能耗自适应聚类层次协议(Low Energy Adaptive Clustering Hierarchy,LEACH)及其变种算法进行对比。实验结果表明,IAFCA在网络寿命和能量效率方面均优于传统的WSN分簇算法,有效延长了网络寿命,降低了节点能耗。
Aiming at the problem of unbalanced energy consumption of Wireless Sensor Networks(WSN),a clustering algorithm IAFCA based on improved artificial fish swarm algorithm(IAFSA)and fuzzy C-means(FCM)is proposed.Firstly,IAFSA improves the search field of view and step size to prevent the iterative solution from falling into the local optimal solution,and uses the final iterative solution as the initial clustering center of FCM to overcome the sensitivity of FCM to the initial solution.Secondly,on the basis of determining the optimal number of cluster heads,FCM effectively establishes the uncertainty relationship between nodes and cluster centers according to the distance similarity between nodes,and rationally clusters WSN nodes.Finally,the cluster head is elected according to the relative remaining energy of the node and the distance to the cluster center.IAFCA conducted experiments in two scenarios,and compared with low energy adaptive clustering hierarchy(LEACH)and its variant algorithms.Experimental results show that IAFCA surpasses the traditional WSN clustering algorithm in terms of network lifetime and energy efficiency,effectively prolonging the WSN network lifetime and reducing node energy consumption.
作者
常宇飞
宋彬杰
陈欣鹏
朱元兴
CHANG Yufei;SONG Binjie;CHEN Xinpeng;ZHU Yuanxing(Unit 32153, Zhangjiakou 075100, China;Army Artillery and Air Defense Academy, Zhengzhou 450002, China)
出处
《信息工程大学学报》
2022年第1期18-23,共6页
Journal of Information Engineering University
基金
国家自然科学基金资助项目(61901284)。
关键词
无线传感器网络
人工鱼群
模糊C均值
分簇算法
wireless sensor networks
artificial fish swarm algorithm
fuzzy C-means
clustering algorithm
