Energy‐efficient data reconstruction algorithm for spatially‐ and temporally correlated data in wireless sensor networks

Abstract

This study introduces a novel algorithm for reconstruction of wireless sensor networks data which inherently have spatial and temporal correlations. The authors' algorithm is based on compressed sensing (CS) and benefits from sliding window processing. This new algorithm rearranges the data in form of a cube and uses this representation to extract more information about the data. There are two optimisation loops which are solved simultaneously and periodically reconstruct one part of the whole signal from measurements that arrive at the sink. In particular, the first reconstruction loop, which uses a modified version of basis pursuit reconstruction algorithm, is meant for reconstruction of a temporal data which is extracted from the data cube, and the second loop which uses a modified version of reweighted l 1 -norm algorithm is for reconstruction of data windows. The authors used a special kind of binary sparse random measurement matrices for sampling which is equipped with a condition to get samples as variously as possible and this, in turn, balances the duty among sensors and provides more information from the field. Simulation results verify that the proposed algorithm achieves better reconstruction accuracy and less energy consumption in comparison with state-of-the-art CS reconstruction methods.