Energy-Efficient Data Recovery via Greedy Algorithm for Wireless Sensor Networks

Abstract

Accelerating energy consumption and increasing data traffic have become prominent in large-scale wireless sensor networks (WSNs). Compressive sensing (CS) can recover data through the collection of a small number of samples with energy efficiency. General CS theory has several limitations when applied to WSNs because of the high complexity of its ℓ1-based conventional convex optimization algorithm and the large storage space required by its Gaussian random observation matrix. Thus, we propose a novel solution that allows the use of CS for compressive sampling and online recovery of large data sets in actual WSN scenarios. The ℓ0-based greedy algorithm for data recovery in WSNs is adopted and combined with a newly designed measurement matrix that is based on LEACH clustering algorithm integrated into a new framework called data acquisition framework of compressive sampling and online recovery (DAF-CSOR). Furthermore, we study three different greedy algorithms under DAF-CSOR. Results of evaluation experiments show that the proposed sparsity-adaptive DAF-CSOR is relatively optimal in terms of recovery accuracy. In terms of overall energy consumption and network lifetime, DAF-CSOR exhibits a certain advantage over conventional methods.