Random sampling in collaborative and distributed mobile sensor networks utilizing compressive sensing for scalar field mapping

In this paper, we propose an algorithm supporting distributed mobile sensor networks (MSN) for scalar field mapping that has many applications such as environmental monitoring or battle field surveillance, etc. We exploit the integration between compressive sensing (CS) and the collaboration of the mobile sensors. In the algorithm each distributed mobile sensor measures at random positions in the sensing area to create one CS measurement and finally shares the measurement with others by communicating through its neighbors. The convergence time is considered while the sensors exchange their measurements. After all the sensors achieve the number of CS measurements needed, a CS recovery algorithm is applied at each mobile sensor to reconstruct sensory readings from all the positions in the sensing area that need to be observed. The total communication energy consumption is formulated, analyzed and simulated.