Adaptive sampling and sensing approach with mobile sensor networks

This paper presents an adaptive sampling and sensing method for mobile sensor networks to reduce the number of measurements and the sampling cost based on compressive sensing. Compressive sensing always uses random measurements, whose information amount cannot be determined previously. The proposed adaptive sampling and sensing approach can find the most informative measurements in unknown environments and reconstruct the original signals. With mobile sensors, measurements are collected sequentially and organically, which give the chance to optimize each of them uniquely. When a mobile sensor is about to collect a new measurement from the surrounding environment, existing information is shared among networked sensors so that each sensor would have a global view of the entire environment. Shared information is analyzed under a sparse domain to infer a model of the environments. The most informative measurements can be determined by optimizing the model. The simulation results demonstrate the effectiveness and the adaptability of the proposed, theoretically-correct algorithm