Minimizing Energy Consumption in Random Walk Routing for Wireless Sensor Networks utilizing Compressed Sensing

Random walk (RW) routing for Wireless Sensor Networks (WSNs) has been proven to balance energy consumption for the whole sensors. Since Compressive sensing (CS) provides a novel idea that can reconstruct all raw data based on a small number of measurements, the energy consumption for data gathering in WSNs is reduced significantly. The combination between RW routing and CS can help efficiently save energy and achieve longer network lifetime. In this paper, we continue to introduce RW as an effective routing method in WSNs utilizing CS. We formulate the mean value of the communication distance between sensors in a RW and the mean distance between RWs and the base station (BS) statistically. We finally build the total energy consumption and exploit the minimum energy consumption case for the network. Based on analyzing the sensor broadcasting radius, while the WSN is connected as an undirected graph G(V, E), we can suggest the optimal radius that leads the network consumes the least energy and even has load balancing.