WSN中基于压缩感知的数据收集方案

提出一种基于压缩感知的数据收集方案.依据感知数据的空间相关性分析,计算出事件发生的区域范围.基于剩余能量的成簇算法对区域范围内的节点进行分簇.各个节点将感知到的原始数据,基于压缩感知理论,进行数据的稀疏表示并采用随机高斯矩阵进行观测,将其观测值发送和存储在簇头节点上,当有移动收集者进入簇头的通信范围后,进行数据收集.理论分析和仿真实验结果表明,该方案能有效延长网络生命周期.

Data Gathering Scheme Based on Compressive Sensing in Wireless Sensor Networks

Data collection in Wireless Sensor Network(WSN) research is the basic problem.This paper presents a data collection solution based on compressive sensing technique and mobile data collector.The regional scope of the incident is calculated by the analysis of spatial correlation of data,and a clustering algorithm is proposed based on residual energy of the nodes to carve up the region events.On this basis,each node senses the original data,based on the theory of compressed sensing.The data is sparse representation and observations by adopting Gaussian Random Matrix(GRM),and its observations are sent and stored in its cluster head,when a mobile collector enters the cluster head communication range,achieving the data collection.Theoretical analysis and simulation experimental results show that this scheme is energy efficient,and can effectively extend the network lifetime.