WSN中基于离散人工鱼群的分簇拓扑优化算法  

A Hierarchical Clustering Topology Optimization Algorithm Based on DAFS in WSN

作　　者：阎新芳[1] 张晓丹[1] 严晶晶[1] 冯岩[1] 

机构地区：[1]郑州大学信息工程学院,河南郑州450001

出　　处：《郑州大学学报（工学版）》2017年第4期69-72,共4页Journal of Zhengzhou University（Engineering Science）

基　　金：河南省科技厅基础与前沿研究计划资助项目(152300410023)

摘　　要：针对无线传感器网络中的HCAGG未综合考虑邻居节点的距离和能量分布,离簇首节点较远而能量较少的节点易成为盲节点的问题,提出一种分级簇算法.该算法引入新的综合权值计算方式,利用离散人工鱼群算法快速遍历到满足成员节点距其越远能量越多,反之越少的新簇头,降低了盲节点出现的概率.仿真结果表明,该算法有助于均衡节点能量,能有效延长网络生存期.To solve the problem in HCAGG that the nodes far away from the cluster head had less energy, and were are prone to be blind nodes, a Hierarchical Clustering Topology Optimization based on Discrete Artificial Fish Swarm （HCTO-DAFS） was proposed. The HCTO-DAFS introduced a new comprehensive weights and ac- quired the new cluster heads. The member nodes would have more energy when they were away from the cluster head. The DAFS could reduce the probability of blind nodes. Simulation experiments demonstrated that this al- gorithm could efficiently balance the nodes＇ energy and prolong the network＇ s lifetime.

关 键 词：WSN 分簇拓扑优化 离散人工鱼群 HCAGG 均衡节点能量 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]