关键词: 无线传感器网络, K-means均衡分簇, 压缩感知

Abstract: 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.

Key words: wireless sensor network, K-means clustering algorithm, compressed sensing