基于改进模糊C均值的能量均衡LEACH算法  

Energy-equalized LEACH algorithm based on improved fuzzy C-means

作　　者：王宗山 李波[1] 保利勇[1] 李艾珊 丁洪伟[1] WANG Zongshan;LI Bo;BAO Liyong;LI Aishan;DING Hongwei(School of Information Science and Engineering,Yunnan University,Kunming 650504,China;Department of Electronic Engineering,Fudan University,Shanghai 200433,China)

机构地区：[1]云南大学信息学院,云南昆明650504 [2]复旦大学电子工程系,上海200433

出　　处：《现代电子技术》2021年第11期13-18,共6页Modern Electronics Technique

基　　金：国家自然科学基金资助项目(61461053);国家自然科学基金资助项目(61461054);国家自然科学基金资助项目(61072079)。

摘　　要：针对LEACH算法随机选取簇首、分簇数量不合理导致网络能耗大、吞吐量低等问题,提出一种改进的LEACH算法(GFCR-LEACH)。网络初始化阶段,基站采用遗传算法优化的模糊C均值聚类算法对网络节点进行聚类分簇,在每个簇内利用考虑节点位置信息和剩余能量的LEACH算法选举簇首,并改善有资格竞选簇首的节点集。数据传输阶段,节点根据自身地理位置选择当前轮次的通信对象。簇内通信阶段引入轮询机制。通过仿真比较GFCR-LEACH算法、LEACH算法和基于Fuzzy C-Means的改进LEACH算法,GFCR-LEACH分簇效果理想,有效地降低了网络能耗,延长了网络生命周期,提高了网络吞吐量。In the LEACH(low energy adaptive clustering hierarchy)algorithm,the cluster heads are selected randomly and the clustering quantity is unreasonable,which result in high network energy consumption and low throughput,so an improved LEACH algorithm(GFCR-LEACH)is proposed.In the network initialization phase,the fuzzy C-means clustering algorithm optimized by genetic algorithm is adopted by the base station to cluster the network nodes.In each cluster,the LEACH algorithm which considers the location information and residual energy of each node is used to select cluster heads and improve the node set which has a qualification to run for the cluster heads.In the data transmission phase,the communication object of the current round is selected for the nodes according to the nodes′geographical location.A polling mechanism is introduced in the intra-cluster communication phase.The GFCR-LEACH algorithm,the LEACH algorithm,and the improved LEACH algorithm based on fuzzy C-means are compared by simulation.The results show that the GFCR-LEACH algorithm has an ideal clustering effect,which can effectively reduces network energy consumption,prolongs network life cycle and improves network throughput.

关 键 词：GFCR-LEACH 算法优化 簇首选举 轮询机制 节点集改善 仿真比较 

分 类 号：TN99-34[电子电信—信号与信息处理] TP393[电子电信—信息与通信工程]