Spatiotemporal Data Gathering Based on Compressive Sensing in WSNs

Abstract

In wireless sensor networks (WSNs), sensor readings have spatiotemporal correlation, and wireless links are unreliable. To balance the energy-performance trade-off and reduce the impact of packet loss, a novel approach is proposed that combines Kronecker compressed sensing (KCS) and cluster topology to exploit spatial and temporal correlations simultaneously. The head nodes generate sparse sub-measurement matrices based on gathered data to avoid measuring nodes that cannot successfully transmit readings. The sink constructs those matrices as a block diagonal matrix (BDM) to utilize the spatial correlation among clusters. Numerical results show that this scheme effectively balances the energy-performance trade-off and maintains a high reconstruction accuracy with packet loss.