基于混合压缩感知的分簇式网络数据收集方法

摘要

In order to reduce the number of transmissions and balance the network load in wireless sensor network,this paper presents a data collection method by using hybrid compressive sensing (cs) in clustering network.Firstly we choose some nodes that are close to the temporary cluster-centroid as the candidate cluster head(CH),secondly determine the CH nodes on the basis of the distance of the candidate nodes to determined CH orderly.Then the common sensor nodes join their nearest cluster.Lastly we build a data transmission tree root of sink node that connects to all CHs greedy.When the number of data transmissions is higher than the threshold,nodes transmit data by using CS.On scenarios of compressive ratio equals 10,the simulation results demonstrate that the number of transmissions for the proposed method is 75% and 65% less than that of Clustering without CS and SPT without CS,35% and 20% less than that of SPT with Hybrid CS and Clustering with Hybrid CS;The standard deviation of nodes transmissions for the proposed method 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.%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%.