基于多目标优化的WSN簇首选择算法  

WSN Cluster Head Selection Algorithm Based on Multi Target Optimization

作　　者：吴勇[1] 张灵[1] 

机构地区：[1]广东工业大学计算机学院,广州510006

出　　处：《传感技术学报》2016年第7期1062-1067,共6页Chinese Journal of Sensors and Actuators

基　　金：广州市科技计划科学研究项目(2014J4100228)

摘　　要：分簇思想可以很好的用于优化路由算法,现有的分簇算法簇首轮换选举大多数只是从簇首和基站之间距离、节点密集度、剩余能量、节点位置等指标来进行改进,没有考虑候选簇头距离各个簇首的平均距离。簇间转发数据包会消耗大量能量,是影响网络性能的一个重要因素。针对目前簇首轮换选举算法存在的不足,提出了一种综合考虑簇内和簇间两个优化目标的算法,此种算法本文简称为DEDS。建立了候选节点剩余能量、候选簇头节点距各个簇首节点平均距离等多目标概率模型作为簇首轮换选择依据。通过在NS2仿真平台上验证了该算法在时延、分组递交率、能耗、稳定性等网络性能优于其它分簇算法。Clustering can be very good for optimizing the routing algorithm,existing clustering algorithm cluster head rotation electoral majority only from the distance between the base station and the cluster head ,node density, residual energy,node position and other indicators to improve,without considering the average distance between the candidate cluster head at various distances from the cluster head. Forwarding packets between clusters will consume a lot of energy is an important factor affecting network performance. In view of the shortcomings of the current cluster algorithm rotation exists,this paper presents an in considering the two clusters and between cluster algorithm optimization goals,such algorithm referred to as DEDS. The establishment of a candidate node residual energy,the candidate cluster head node average distance from each cluster head and other objective probability model selection as a cluster head rotation basis. On the NS2 simulation platform validated by the algorithm delay,packet delivery rate,energy consumption,network performance stability than other clustering algorithms.

关 键 词：无线传感器网络 簇首轮换 簇间平均距离 候选节点剩余能量 NS2 DEDS 

分 类 号：TP393.01[自动化与计算机技术—计算机应用技术]