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

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. Various DDS schemes based on compressive sensing (CS) have been proposed to reduce the number of transmission/receptions to improve network´s area energy efficiency. However, these schemes assume that sensor readings are compressible in the discrete cosine transformation (DCT) domain, whereas our experimental results validate that this assumption cannot be established in a real WSN scenario and the performance of the practical sensor readings recovery will be significantly degraded. To address this problem, this paper proposes a novel DDS scheme termed as practical spatiotemporal compressive network coding (P-STCNC). Our idea is to adaptively train the sparse dictionaries to sparsify practical sensor readings as well as optimize corresponding measurement matrices in both spatial and temporal domains to guarantee accurate data recovery. Simulation results based on real datasets demonstrate the effectiveness of the proposed scheme.