基于改进K-means和熵权法的WSN分簇路由算法  

WSN Clustering Routing Algorithm Based on Improved K-means and Entropy Weight Method

作　　者：方旺盛[1] 王旭 FANG Wangsheng;WANG Xu(School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000)

机构地区：[1]江西理工大学信息工程学院,赣州341000

出　　处：《计算机与数字工程》2025年第3期623-627,683,共6页Computer & Digital Engineering

基　　金：国家自然科学基金项目(编号:62062037)资助。

摘　　要：针对无线传感器网络能量有限、负载不均衡的问题,提出一种基于改进K-means和熵权法的WSN分簇路由算法(IKEW)。该算法在成簇阶段利用密度法和最大最小距离对K-means算法进行改进,并采用重分配方案平衡各簇节点的数量。在簇头选取阶段,采用熵权法计算各节点指标的权重,使选出的簇头更加合理。在数据传输阶段,根据簇头的剩余能量和数据的传输距离构造通信消耗函数来选择中继节点。仿真实验结果表明:提出的算法能够有效地均衡网络能耗,延长网络生命周期。Aiming at the problems of limited energy and unbalanced load of the wireless sensor network,a WSN clustering routing algorithm based on improved K-means and entropy weight method(IKEW)is proposed.During the clustering phase,this algorithm improves the K-means algorithm using density-based methods and maximum-minimum distance,and adopts a reassignment scheme to balance the number of nodes in each cluster.During the cluster head selection phase,the entropy weight method is used to calculate the weight of each node index,making the selection of cluster heads more reasonable.During the data transmission phase,a communication consumption function is constructed based on the cluster head's remaining energy and the data's transmission distance to select relay nodes.Simulation results show that the proposed algorithm can effectively balance network energy consumption and prolong the network lifetime.

关 键 词：无线传感器网络 K-MEANS 节点重分配 熵权法 负载均衡 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]