Exploiting Temporal Correlation of Sparse Signals in Wireless Sensor Networks

Abstract

Collecting data continuously in Wireless Sensor Networks (WSNs) with limited power and bandwidth is still a challenging issue. Recently, the sparse nature of these data motivated the use of Compressive Sensing (CS) as an efficient data gathering technique. In this paper, several algorithms are proposed to effectively exploit the temporal correlation and the sparsity inherent in sensor network data over time. These algorithms combine recent advances in compressive sensing (CS) theory, data compression, and data gathering algorithms. Experimental analysis through simulation evinces that the proposed algorithms significantly reduce the power consumption by reducing the number of sent measurements for the same Normalized Mean Square Error (NMSE).