一种基于反馈的K-means分簇算法研究

A Research On K-means Clustering Algorithm Based On Feedback

  • 摘要: 针对典型的LEACH分簇式路由协议分簇不均匀,簇头节点分布随机导致网络能量消耗大的情况,本文提出一种基于死亡节点数目反馈的K-means分簇算法。首先通过K-means算法划分簇的个数,选择簇的中心节点为该簇的簇头,并通过位置集中性得到集中性较大的若干个节点为主簇头群,其中最大的为主簇头,自此完成初始化。此后用一个受死亡节点数调控的自适应打分函数更新每一轮的簇头和主簇头。主簇头只用于融合并传输数据并不负责感知环境信息。仿真实验结果表明:本算法相较LEACH以及传统的基于K-means的分簇算法,在整个网络的生存时间上分别提高了35%和25%。同时证明:反馈机制的加入和主簇头的选取都有利于网络寿命的提升。

     

    Abstract: Clusters in the classical LEACH routing protocol are not uniform and cluster heads are random in wireless sensor network, so we proposed a K-means clustering algorithm based on feedback of the number of dead nodes. In the paper,K-means-based clustering was used for cluster formation and the center node of the cluster was selected as the cluster head. We got the several nodes as super cluster heads based on location centrality . Among them,the biggest one was chosen as SCH. Later on,We only used adaptive scoring function controlled by the number of dead nodes to select cluster heads. The function of super-cluster heads was just to aggregate information and transfer it. Simulation results show that compared to LEACH and traditional method based on K-means, the proposed algorithm improved the lifetime of network by 35% and 25% respectively. It was also proved that feedback mechanism and super-cluster heads were beneficial to the improvement of the network lifetime.

     

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