计算机工程 ›› 2018, Vol. 44 ›› Issue (10): 129-135.doi: 10.19678/j.issn.1000-3428.0047270

• 移动互联与通信技术 • 上一篇    下一篇

分簇感知网络中基于压缩感知的数据收集方法

李玉龙,刘任任,赵津锋,臧浪,曹斌   

  1. 湘潭大学 信息工程学院,湖南 湘潭 411105
  • 收稿日期:2017-05-19 出版日期:2018-10-15 发布日期:2018-10-15
  • 作者简介:李玉龙(1990—),女,硕士研究生,主研方向为无线传感器网络;刘任任,教授、博士、博士生导师;赵津锋、臧浪、曹斌,硕士研究生。

Data Collection Method in Clustering Sensing Network Based on Compressive Sensing

LI Yulong,LIU Renren,ZHAO Jinfeng,ZANG Lang,CAO Bin   

  1. College of Information Engineering,Xiangtan University,Xiangtan,Hunan 411105,China
  • Received:2017-05-19 Online:2018-10-15 Published:2018-10-15

摘要: 为减少分簇感知网络数据通信量、延长网络生命周期,提出一种结合混合压缩感知(CS)技术的分簇无线传感器网络数据收集方法。该方法按地理位置划分感知区域为若干簇,并假设各簇区域中心存在一个虚拟簇头节点,且选取虚拟簇头节点一跳通信范围内的节点为候选簇头节点,使用Prim算法以sink为根节点连接各虚拟簇头节点生成一棵最小生成树,由sink节点开始,为最小生成树各分支中的簇从候选簇头节点中动态规划选出簇头节点,构造以sink节点为根节点且按最小生成树顺序连接各簇头节点的数据传输骨干树。仿真结果表明,当压缩率为10时,与clustering without CS、 SPT without CS、SPT with hybrid CS和clustering with hybrid CS方法相比,该方法通信量分别减少了65%、55%、40%和10%。

关键词: 分簇感知网络, 压缩感知, 动态规划, 数据收集, 最小生成树, 簇头选取

Abstract: In order to reduce the cluster sensing network transmissions and prolong network lifetime,this paper proposes a data collection method based on the hybrid Compressive Sensing(CS) technology for clustering Wireless Sensor Network(WSN).It divides the sensing area into several clusters according to the geographical location,assuming there is a Virtual Cluster Head(VCH) in the center of each cluster area,and selects the nodes one hop distance from the VCH as Candidate Cluster Head(CCH).A Minimum Spanning Tree(MST),which chooses sink as root node and connects each VCH,is generated by the Prim algorithm.Starting from the sink,it chooses Cluster Head(CH) from CCH for clusters in each branch of the MST using dynamic programming.A backbone tree that connects all CH to the sink in the sequence of MST is constructed.Simulation results prove that,when the compressive ratio is 10,compared with clustering without CS,SPT without CS,SPT with hybrid CS,and clustering with hybrid CS,the reduction ratio of traffic of the method respectively are 65%,55%,40% and 10%.

Key words: clustering sensing network, Compressive Sensing (CS), dynamic programming, data collection, Minimum Spanning Tree(MST), Cluster Head (CH) selection

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