摘要
针对无线传感器网络中传统分层路由协议LEACH因簇头选举不合理与分簇不均匀造成的网络能耗不均衡的问题,文章基于K-means聚类算法(K-means clustering algorithm)提出WSN分簇改进算法LEACH-KTM。该算法主要从簇建立、簇头选举以及数据传输三个方面进行改进。算法采用无线电能量损耗模型建立最优簇头数目表达式,提出三角中线法KTM(K-means Triangle Midline)结合最优簇头数目合理选取初始聚类中心并完成分簇。每轮通过改进的簇头选举阈值函数在簇内进行簇头的选举。数据传输采用单跳与多跳相结合的方式,并且在簇内选举中继节点分担簇头任务。算法提出簇间偏差函数和重新分簇条件,网络运行的过程中满足分簇条件的轮数才需重新分簇。仿真结果表明LEACH-KTM算法与传统算法相比,能耗更加均衡,有效延长了网络的生存周期。
For the problems of uneven energy consumption caused by unreasonable cluster header election and uneven cluster-ing of traditional hierarchical routing protocol LEACH in wireless sensor network,this paper proposes an improved WSN clustering algorithm LEACH-KTM based on K-means clustering algorithm.The algorithm is mainly improved from three aspects:cluster establishment,cluster head election and data transmission.The algorithm uses the radio energy loss model to establish the expression of the optimal number of cluster heads,and proposes that the triangle midline method KTM(K-means Triangle midline)reasonably selects the initial cluster center and completes the clustering by combining the optimal number of cluster heads.Each round is carried out within the cluster by an improved cluster head election threshold function.The data transmission adopts a combination of single-hop and multi-hop,and relay nodes are elected within the cluster to share the cluster head task.The algorithm proposes the intercluster deviation function and the reclustering condition,and the number of rounds that meet the clustering condition during the network operation needs to be reclustered.The simulation results show that the LEACH-KTM algorithm has more balanced energy consumption,which effectively prolongs the life cycle of the network.
作者
尚立信
焦新泉
SHANG Lixin;JIAO Xinquan(State Key Laboratory of Electronic Technology,North University of China,Taiyuan 030051;Key Laboratory of Instrument Science and Dynamic Testing(Ministry of Education),North University of China,Taiyuan 030051)
出处
《计算机与数字工程》
2025年第8期2101-2107,共7页
Computer & Digital Engineering
基金
国家自然科学基金项目(编号:62105305)
山西省基础研究计划项目(编号:20210302123068)资助。
