Distributed compressive and collaborative sensing data collection in mobile sensor networks

Compressive sensing (CS) supports emergent paradigms for data processing in various applications in different fields. In this paper, a distributed compressive and collaborative sensing algorithm is proposed for mobile sensor networks (MSN) to build a scalar field map that significantly reduces power consumption for the sensor movements and communications. Based on CS a certain number of mobile sensors deployed randomly on a field can build a map themselves. They collect sensory data from the others through their neighborhoods to generate CS measurements and then recover all data from the network based on a small number of CS measurements compared to the total readings in the network. All transmission power consumption for the network are analyzed and formulated. We also analyze the convergence time and the trade-off with the sensor communication range and suggest an optimal range for the mobile sensor to consume the least power. (C) 2019 Elsevier B.V. All rights reserved.