Joint mobile energy replenishment and data gathering in wireless rechargeable sensor networks

Recent studies have shown that energy harvesting wireless sensor networks have the potential to provide perpetual network operations by capturing renewable energy from the external environment. However, the spatial-temporal profiles of such ambient energy sources typically exhibit great variations, and can only provide intermittent recharging opportunities to support low-rate data services. In order to provide steady and high recharging rates, and achieve energy-efficient data gathering from sensors, in this paper, we propose to utilize mobility for the joint design of energy replenishment and data gathering. In particular, a multifunctional mobile entity, called SenCar in this paper, is employed, which serves not only as a data collector that roams over the field to gather data via short-range communication but also as an energy transporter that charges static sensors on its migration tour via wireless energy transmissions. Taking advantages of the SenCar's controlled mobility, we give a two-stage approach for the joint design. In the first stage, the locations of a subset of sensors are periodically selected as anchor points, where the SenCar will sequentially visit to charge the sensors at these locations and gather data from nearby sensors in a multi-hop fashion. In order to achieve a desirable balance between the energy replenishment amount and data gathering latency, we provide a selection algorithm to search for a maximum number of anchor points where sensors hold the least battery energy, and meanwhile by visiting them the tour length of the SenCar is no more than a threshold value. In the second stage, we consider data gathering performance when the SenCar migrates among these anchor points. We formulate the problem into a network utility maximization problem and propose a distributed algorithm to adjust data rates, link scheduling and flow routing so as to adapt to the up-to-date energy replenishing status of sensors. The effectiveness of our approach is validated by extensive numerical results. Comparing with solar harvesting networks, our solution can improve the network utility by 48% on average.