Distributed spatial-temporal compressive data gathering for large-scale WSNs

Abstract

Wireless sensor networks (WSNs) are widely used for monitoring physical phenomena of interest, but one of the major challenges for designing sensor networks is to minimize transmission cost with obtaining fidelity information in the sink. Distributed compressive sensing (CS) is a promising in-network data compression technique to reduce data transmission cost and accurately recover sensory data in the sink. In this paper, we propose a distributed spatial-temporal compressive data gathering scheme for large-scale WSNs to improve the recovery quality of sensory data and prolong the sensor network's lifetime as well. In our scheme, we first present a sensory data partition model to improve the compressibility of gathering data. Sparse and dense random projections are used for compressing and gathering different components of our sensory data partition model to obtain the same projection process. We also exploit cluster-based routing strategy to gather CS measurement and reduce energy consumption. Finally, experiment results show that our scheme not only improves the sensory data recovery quality compared with dense random projections data gathering scheme, but also significantly prolongs the sensor network's lifetime compared with tree-type CS based data gathering schemes.