Energy‐efficient data reconstruction algorithm for spatially‐ and temporally correlated data in wireless sensor networks. Issue 8 (1st October 2018)

• Record Type: Journal Article
• Authors: Adnani, Seyedeh Nasim; Tinati, Mohammad Ali; Azarnia, Ghanbar; Rezaii, Tohid Yousefi
• Is Part Of: IET signal processing. Volume 12:Issue 8(2018)
• Page Start: 1053
• Page End: 1062
• Publication Date: 2018-10-01
• Subjects: wireless sensor networks -- energy conservation -- energy consumption -- telecommunication power management -- compressed sensing -- optimisation -- matrix algebra -- signal reconstruction -- signal sampling -- data handling; energy‐efficient data reconstruction algorithm -- spatially‐correlated data -- temporally correlated data -- wireless sensor networks data -- spatial correlation -- temporal correlation -- compressed sensing -- sliding window processing -- optimisation loops -- reconstruction loop -- basis pursuit reconstruction algorithm -- temporal data reconstruction -- reweighted l1 ‐norm algorithm -- data windows reconstruction -- data cube -- binary sparse random measurement matrices -- energy consumption; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2017.0467
• Languages: English
• ISSNs: 1751-9675

Abstract

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.