Energy-efficient mobile data collection in energy-harvesting wireless sensor networks

Abstract

Environmental energy harvesting technologies have provided potential for battery-powered wireless sensor networks to have perpetual network operations. To design a robust network that can adapt to not only temporal but also spatial variations of ambient energy sources, in this paper, we utilize mobility to circumvent communication bottlenecks, by employing a mobile data collector, called SenCar. We propose a two-stage approach for mobile data collection. In the first stage, SenCar makes stops at a subset of selected sensor locations to collect data packets in a multi-hop fashion. We provide a selection algorithm to search for sensor locations with most residual energy while guaranteeing a bounded tour length. Then we design a distributed data gathering algorithm to achieve maximum network utility by adjusting data rates, link scheduling and flow routing that adapts to spatial temporal environmental energy variations. The effectiveness and efficiency of the proposed algorithms are validated by extensive numerical results.