基于压缩感知理论的无线传感器网络数据融合算法

Wireless Sensor Network Data Fusion Algorithm Based on Compressed Sensing Theory

针对传感器节点部署稠密,节点覆盖重叠区域较大,导致采集数据冗余度大的问题,利用节点收集数据的时间和空间相关性,提出一种基于压缩感知理论的无线传感器网络(WSN)数据融合算法,并通过仿真实验分析了其性能.实验结果表明,该算法不仅可以减少簇首的数据传输量,减少了节点的平均能量消耗,延长网络的生存时间,而且性能明显优于对比算法.

Aiming at the dense of sensor nodes,the overlapping area of the node coverage was large,resulting in the problem of large data redundancy.Using temporal and spatial correlation of nodes collect data,we proposed a data fusion algorithm based on compressive sensing theory for wireless sensor network（WSN）,and analyzed its performance via simulation experiment.The experimental results show that the proposed algorithm can not only reduce the data transmission amount of the cluster head and reduce the average energy consumption of node to prolong the survival time of the network,but also the performance is obviously better than the contrast algorithm.