Mobile distributed compressive sensing for data collection in wireless sensor networks

Abstract

Mobile sinks have been shown to balance energy and help to achieve uniform power consumption across wireless sensor networks (WSN). In this paper, a compressive sensing (CS) based algorithm for distributed mobile sensors to collect data from static sensors deployed in a sensing area is proposed. The mobile sensors deployed randomly in the field are capable as mobile sinks. They move randomly in the sensing area to collect sensory data from a certain number of static sensors to create CS measurements. The measurements are exchanged between the mobile sensors based on a defined transmission range. After each mobile sensor obtains all CS measurements from the others, it can implement a CS recovery algorithm to reconstruct all sensor readings from all the static sensors in the network to build a scalar map itself. All power consumption for data transmission between the mobile sensors is formulated, analyzed and simulated. The main elements of the network, including the number of mobile sensors, the transmission range, and the convergence time are analyzed and an optimal case for the network to consume the least power is shown.