Compressive networked storage with lazy-encoding

Abstract

We investigate the problem of distributed networked storage with compressive sensing in wireless sensor networks, and a compressive storage scheme for local data query is proposed. Specifically, we propose a simple but efficient one-step data dissemination strategy, and the dissemination cost is reduced dramatically. We further present a lazy-encoding algorithm, using which the local data are capable of being reconstructed without recovering the global data field if not necessary. Thus the decoding ratio decreases significantly. Experiments using real sensor data show that the proposed scheme achieves far better local data recovery performance compared to the existing ones.