The Cluster based Compressive Data Collection for Wireless Sensor Networks with a Mobile Sink

Abstract This paper considers the problem of data collection in a delay-tolerant wireless sensor network with a mobile sink (MS). The main contribution is a data collection strategy which makes use of the hybrid Compressive Sensing (CS) and clustering: within clusters, raw reading is transmitted; while CS measurement is transmitted between clusters and MS. We provide an analytical model to describe the energy consumed by the nodes, based on which we figure out the optimal cluster radius. We develop two computationally efficient and distributed implementations for this approach, whose message complexities at a node are both O(1). Extensive simulations are conducted to investigate their performance and comparisons with existing methods are also presented. The results show that the proposed approach can improve the network lifetime by about 1.2 and 2 times against the two compared schemes.