Distributed compressive sensing based data gathering in energy harvesting sensor network

Abstract

Wireless sensor networks are gaining popularity in practical monitoring and surveillance applications. One of the major challenges for designing sensor networks is to minimize the transmission cost. Distributed compressive sensing is a promising technique for energy efficient data gathering in wireless sensor networks. In this paper, we propose a distributed compressive sensingbased data gathering scheme in energy harvesting sensor networks, in which the sensor readings possess both inter-(spatial) and intra-(temporal) signal correlations to improve the recovery quality of sensory data and prolong the sensor network’s lifetime as well. Besides, we also consider that the sensors operate with intermittently available energy that is harvested from the environment. A cluster-based routing strategy is exploited and a joint sparsity model is used for compressing the sensory data. Then the Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm is designed to recover the sparse data. The simulation results show significant gain in terms of signal reconstruction accuracy and energy consumption.