Compressive wireless mobile sensing for data collection in sensor networks
In this paper, we exploit an integration between Compressive Sensing (CS) and the random mobility of sensors in distributed mobile sensor networks (MSN) to sparsely sample sensing areas. A small number of mobile sensors are deployed randomly in an area to observe a large number of positions. At each sampling time, the sensors collect data at their random positions and exchange their readings to the others through their neighbors within the sensor transmission range to form one CS measurement at each sensor. After M rounds of moving, sensing and sharing data, each mobile sensor has M CS measurements to be able to reconstruct all readings from all positions that need to be observed. Network performance is analyzed considering the number of sensors deployed in the networks, the convergence time and the sensor transmission range. Expressions for transmission power consumption are formulated and optimal low power cases are identified.