LAUSR

Energy-harvesting wireless sensor network (WSN) is composed of unreliable wireless channels and resource-constrained nodes which are powered by solar panels and solar cells. Energy-harvesting WSNs can provide perpetual data service by harvesting energy from surrounding environments. Due to the random characteristics of harvested energy and unreliability of wireless channel, energy efficiency is one of the main challenging issues. In this paper, we are concerned with how to decide the energy used for data sensing and transmission adaptively to maximize network utility, and how to route all the collected data to the sink along energy-efficient paths to maximize the residual battery energy of nodes. To solve this problem, we first formulate a heuristic energy-efficient data sensing and routing problem. Then, unlike the most existing work that focuses on energy-efficient data sensing and energy-efficient routing respectively, energy-efficient data sensing and routing scheme (EEDSRS) in unreliable energy-harvesting wireless sensor network is developed. EEDSRS takes account of not only the energy-efficient data sensing but also the energy-efficient routing. EEDSRS is divided into three steps: (1) an adaptive exponentially weighted moving average algorithm to estimate link quality. (2) an distributed energetic-sustainable data sensing rate allocation algorithm to allocate the energy for data sensing and routing. According to the allocated energy, the optimal data sensing rate to maximize the network utility is obtained. (3) a geographic routing with unreliable link protocol to route all the collected data to the sink along energy-efficient paths. Finally, extensive simulations to evaluate the performance of the proposed EEDSRS are performed. The experimental results demonstrate that the proposed EEDSRS is very promising and efficient.