基于模糊逻辑推理系统的簇首选择算法  

A cluster head selection algorithm based on fuzzy logic inference system

作　　者：朱国晖 杨瑛 ZHU Guohui;YANG Ying(School of Communications and Information Engineering,Xi'an University of Posts and Telecommunications,Xi'an 710121,China)

机构地区：[1]西安邮电大学通信与信息工程学院,陕西西安710121

出　　处：《西安邮电大学学报》2021年第6期97-103,共7页Journal of Xi’an University of Posts and Telecommunications

基　　金：国家自然科学基金项目(61371087)。

摘　　要：在车联网分簇算法的簇首选择中,对节点运动的差异性影响簇结构稳定性的问题进行分析,提出一种基于模糊逻辑推理系统的簇首选择算法。分别从节点的运动角度、位置角度和环境角度综合分析,选择车辆的相对运动速度、相对中心度和相对邻居节点数等3个参数作为系统输入,通过模糊逻辑合成模糊输出集合并进行解模糊化,得到节点成为簇首的优先级,选择参数最接近网络平均值的节点作为簇首。仿真结果表明,所提簇首选择算法提升了簇首的平均生存时间,增加了簇结构的稳定性。In the cluster head selection for the clustering algorithm of the vehicular ad-hoc networks(VANETs),the problem that the difference of node motion affects the stability of the cluster structure is analyzed.A cluster head selection algorithm is proposed based on fuzzy logic inference system.Comprehensively analyze from the angle of node motility,position and environment,to select the relative motion of the vehicle speed,the relative center degree and the relative neighbor node number as the three parameters of the system input.Synthesis fuzzy set and anti-fuzz through fuzzy logic,to obtain the priority of the cluster head.The node whose parameter is closest to the network average value is selected as the cluster head.Simulation results show that the proposed cluster head selection algorithm can extend the average survival period,and can improve the stability of the cluster structure.

关 键 词：车联网 簇首选择 模糊逻辑 分簇算法 相对中心度 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]