Adaptive sampling using mobile sensor networks

This paper presents an adaptive sparse sampling approach and the corresponding real-time scalar field reconstruction method using mobile sensor networks. Traditionally, the sampling methods collect measurements without considering possible distributions of target signals. A feedback driven algorithm is discussed in this paper, where new measurements are determined based on the analysis of existing observations. The information amount of each potential measurement is evaluated under a sparse domain based on compressive sensing framework given all existing information shared among networked mobile sensors, and the most informative one is selected. The efficiency of this information-driven method falls into the information maximization for each individual measurement. The simulation results show the efficacy and efficiency of this approach, where a scalar field is recovered.