Adaptive low power detection of sparse events in wireless sensor networks

Compressive Sensing (CS) has recently opened the door for efficient algorithms to solve various data gathering problems. Among these problems is sparse events detection in wireless sensor networks. In this problem, it is desirable to reduce the sensing cost by minimizing the number of sensors and the amount of data sent by each sensor. In this paper, we model the problem of sparse event detection as a compressive support recovery problem. We exploit the sparse and the binary nature of the event signal in the reconstruction algorithm using sequential compressive sensing. This provides an efficient solution to the problem, even under the assumptions of wide sensing area and high levels of noise. Simulation results show an improved performance under different compression ratios as compared to previous CS based approaches. It also shows the robustness of the proposed approach at low SNRs.