WSN中基于压缩感知的分簇数据收集算法

为减少无线传感器网络的数据通信量和能量消耗，基于WSN节点数据时空相关性的特性，提出一种将K-means均衡分簇和CS理论相结合的数据收集方法。首先,通过K-means聚类算法均匀划分网络成簇。然后，各簇首对采集到的数据进行基于时空相关性的压缩感知并传输至基站Sink节点。最后，Sink节点采用OMP算法对收集到的数据进行精准重构。仿真结果表明,该算法有效减少了无线传感器网络的数据通信量和压缩感知算法重构过程所需要的观测量。

A Clustering Data Collection Algorithm in WSN Based on Compressed Sensing

In order to reduce the wireless sensor network transmissions and its energy consumption, a data collection method combining K-means balanced clustering and Compressed Sensing (CS) theory is proposed based on the characteristics of spatio-temporal correlation of WSN node data. Firstly, K-means clustering algorithm is used to divide the network into clusters. Then, each cluster head node transfers the collected data to the Sink node of the base station based on the spatial-temporal CS. Finally, Sink node uses OMP algorithm to accurately reconstruct the collection data. The simulation results show that this algorithm effectively reduces the data traffic of wireless sensor network and measurement required in the reconstruction of compressed sensing algorithm.