Energy-Efficient Distributed Data Storage for Wireless Sensor Networks Based on Compressed Sensing and Network Coding

Abstract

Recently, distributed data storage (DDS) for Wireless Sensor Networks (WSNs) has attracted great attention, especially in catastrophic scenarios. Since power consumption is one of the most critical factors that affect the lifetime of WSNs, the energy efficiency of DDS in WSNs is investigated in this paper. Based on Compressed Sensing (CS) and network coding theories, we propose a Compressed Network Coding based Distributed data Storage (CNCDS) scheme by exploiting the correlation of sensor readings. The CNCDS scheme achieves high energy efficiency by reducing the total number of transmissions Nttot and receptions Nrtot during the data dissemination process. Theoretical analysis proves that the CNCDS scheme guarantees good CS recovery performance. In order to theoretically verify the efficiency of the CNCDS scheme, the expressions for Nttot and Nrtot are derived based on random geometric graphs (RGG) theory. Furthermore, based on the derived expressions, an adaptive CNCDS scheme is proposed to further reduce Nttot and Nrtot. Simulation results validate that, compared with the conventional ICStorage scheme, the proposed CNCDS scheme reduces Nttot, Nrtot, and the CS recovery mean squared error (MSE) by up to 55%, 74%, and 76% respectively. In addition, compared with the CNCDS scheme, the adaptive CNCDS scheme further reduces Nttot and Nrtot by up to 63% and 32% respectively.