Spatiotemporal Compressive Network Coding for Energy-Efficient Distributed Data Storage in Wireless Sensor Networks

Abstract

Distributed data storage (DDS) provides a promising approach to the reliable recovery of the whole sensor readings in a wireless sensor network (WSN) by visiting a small subset of sensor nodes. To reduce the number of transmissions/receptions, various DDS schemes based on compressive sensing (CS) have been proposed in the literature. However, these schemes only exploit the spatial correlation among sensor readings from geographically neighboring nodes, and the potential temporal correlation over multiple time slots within a frame duration is ignored. This results in energy inefficiency of DDS within the WSN. In this letter, we take a new approach, and exploit spatial and temporal (spatiotemporal) correlations among sensor readings simultaneously. A novel DDS coding scheme, referred to as spatiotemporal compressive network coding (ST-CNC), is proposed to collect sensor readings across the WSN in a more energy-efficient manner. Compared with the existing DDS schemes with CS, the proposed scheme significantly reduces the number of transmissions and receptions with similar recovery performance.