Novel Energy-Efficient Data Gathering Scheme Exploiting Spatial-Temporal Correlation for Wireless Sensor Networks

Abstract

A novel energy-efficient data gathering scheme that exploits spatial-temporal correlation is proposed for clustered wireless sensor networks in this paper. In the proposed method, dual prediction is used in the intracluster transmission to reduce the temporal redundancy, and hybrid compressed sensing is employed in the intercluster transmission to reduce the spatial redundancy. Moreover, an error threshold selection scheme is presented for the prediction model by optimizing the relationship between the energy consumption and the recovery accuracy, which makes the proposed method well suitable for different application environments. In addition, the transmission energy consumption is derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the existing schemes, and the sink can recover measurements with reasonable accuracy by using the proposed method.