计算机与现代化

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WSN中多节点RSS混合序列聚类算法研究

  

  1. 江南大学物联网工程学院,江苏无锡214122
  • 收稿日期:2015-11-26 出版日期:2016-05-24 发布日期:2016-05-25
  • 作者简介:陈树(1969-),男,江苏淮安人,江南大学物联网工程学院副教授,研究方向:过程控制与优化,现场总线及控制技术,无线传感器网络及通信; 陆颖(1991-),男,江苏常州人,硕士研究生 ,研究方向:无线传感器网络。
  • 基金资助:
    江苏省六大人才高峰基金资助项目(2012-WLW-006)

Clustering Algorithm Research on Mixed Received Signal Strength Sequences #br# of Multiple Nodes on Wireless Sensor Network

  1. College of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China
  • Received:2015-11-26 Online:2016-05-24 Published:2016-05-25

摘要:

移动锚节点通过接收的节点RSS混合序列聚类确定无标识未知节点个数,RSS混合序列的聚类效果直接决定无线传感器网络节点的定位精度。本文针对EM算法处理RSS高斯混合序列中存在初值敏感的
不足,采用三阶段迭代处理方法,对EM算法增加前道和后道处理,K均值作为前道处理改善EM初值选取,贝叶斯信息准则作为后道处理提高聚类的精度。仿真结果表明,本文算法可以成功估算无标识未知
节点个数,并且获取精确的接收信号强度。

关键词: 接收信号强度, 期望最大值算法, K均值, 贝叶斯信息准则

Abstract:

It confirms number of unknown nodes without identification by dealing with mixed received signal strength sequences achieved by mobile anchor node, the clustering
results of the mixed received signal strength directly determines the positioning accuracy of unknown nodes on the wireless sensor network. EM algorithm is sensitive to the
initial values. To overcome the drawback above, this paper proposes a three stage iterative processing method, adding front and rear channel to EM algorithm. Kmeans algorithm
is used as front processing channel to improve the selection of initial values on EM algorithm. Bayesian information criterion is employed as the rear processing channel to
improve the accuracy of the clustering. Finally, the simulation result shows that the proposed algorithm can successfully estimate the number of unknown nodes, and achieve
precise received signal strength.

Key words:  , received signal strength; EM algorithm; Kmeans; Bayesian information criterion

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