基于混合压缩感知的分簇式网络数据收集方法

发布时间：2018-12-23 10:35

【摘要】：为了减少分簇式传感器网络中的数据传输量并均衡网络负载,提出了一种采用混合压缩感知(compressive sensing,CS)进行数据收集的方法.1)选取各临时簇中距离簇质心最近的一些节点为候选簇头节点,然后依据已确定的簇头节点到未确定的候选簇头节点的距离依次确定簇头;2)各普通节点选择加入距离自己最近的簇中;3)贪婪构建一棵以Sink节点为根节点并连接所有簇头节点的数据传输树,对数据传输量高于门限值的节点使用CS压缩数据传输.仿真结果表明:当压缩比率为10时,数据传输量比Clustering without CS和SPT without CS分别减少了75%和65%,比SPT with Hybrid CS和Clustering with Hybrid CS分别减少了35%和20%;节点数据传输量标准差比Clustering without CS和SPT without CS分别减少了62%和81%,比SPT with Hybrid CS和Clustering with Hybrid CS分别减少了41%和19%.
[Abstract]:In order to reduce the amount of data transmission in cluster sensor networks and balance the network load, a hybrid compression sensing (compressive sensing, is proposed. CS). 1) selecting some nodes nearest to the center of cluster as candidate cluster head nodes, and then determining the cluster head according to the distance from the determined cluster head node to the undetermined candidate cluster head node. 2) each common node chooses to join the cluster nearest to itself; 3) A data transmission tree with Sink node as root node and connecting all cluster head nodes is constructed greedily, and the data transmission is compressed by CS for nodes whose data transmission amount is higher than the threshold. The simulation results show that when the compression ratio is 10:00, the amount of data transmission is 75% and 65% less than that of Clustering without CS and SPT without CS, and 35% and 20% less than that of SPT with Hybrid CS and Clustering with Hybrid CS, respectively. The standard deviation of node data transmission is 62% and 81% less than that of Clustering without CS and SPT without CS, 41% and 19% less than that of SPT with Hybrid CS and Clustering with Hybrid CS, respectively.
【作者单位】：湘潭大学信息工程学院;江苏省无线传感网高技术研究重点实验室(南京邮电大学);智能计算与信息处理教育部重点实验室(湘潭大学);湖南大学信息科学与工程学院;
【基金】：国家自然科学基金项目(61379115,61110215,61372049,61602398) 湖南省自然科学基金项目(2015JJ4047,12JJ9021,13JJ8006) 江苏省无线传感网高技术研究重点实验室开发课题(WSNLBKF201501) 湖南省重点学科建设基金项目~~
【分类号】：TP212.9;TN929.5

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