Neighbor-Aided Spatial-Temporal Compressive Data Gathering in Wireless Sensor Networks

Abstract

The integration between data collection methods in wireless sensor networks (WSNs) and compressive sensing (CS) provides energy efficient paradigms. Single-dimensional CS approaches are inapplicable in spatial and temporal correlated WSNs while the Kronecker compressive sensing (KCS) model suffers performance degradation along with the increasing data dimensions. In this letter, a neighbor-aided compressive sensing (NACS) scheme is proposed for efficient data gathering in spatial and temporal correlated WSNs. During every sensing period, the sensor node just sends the raw readings within the sensing period to a randomly and uniquely selected neighbor. Then, the CS measurements created by the neighbor are sent to the sink node directly. The equivalent sensing matrix is proved to satisfy both structured random matrix (SRM) and generalized KCS models. And, by introducing the idea of SRM to KCS, the recovery performance of KCS is significantly improved. Simulation results demonstrate that compared with the conventional KCS models, the proposed NACS model can achieve vastly superior recovery performance and receptions with much fewer transmissions.