Target monitoring in wireless sensor networks using compressive sensing

The events are relatively sparse compared with the number of sources in wireless sensor networks. In order to reduce deployment cost, the number of sensors is limited, because sensor has limit energy, so not all the sensors are turned on all the time. In this paper, a model is introduced to formulate the problem of target detection in wireless sensor networks through a compressive sensing method. The number of wake-up sensors can be greatly reduced accompany with the number of sparse events decrease; sparse event is much smaller than the total number of sources. We use binary nature to indicates a target is found or not, and use OPM algorithm to recovery sample signal .Finally, we analyze and compare the performance of the model through compressive sensing algorithms at different condition. Simulation result show that the sampling rate can reduce accompany with the target reduce without sacrificing performance. With further decreasing the sampling rate, the performance is gradually reduced.