Joint Mobile Energy Replenishment with Wireless Power Transfer and Mobile Data Gathering in Wireless Rechargeable Sensor Networks

Abstract

Energy replenishment is now experiencing rapid development and great proliferation in rechargeable sensor networks, which has the potential to provide perpetual network operations by capturing renewable energy from external environments. However, the low output of energy capturing devices can only provide intermittent recharging opportunities to support low-rate data services due to spatial–temporal, geographical, or environmental factors. In this chapter, we study the problem of how to provide steady and high recharging rates by employing wireless power transfer and simultaneously achieve optimized data gathering performance in wireless rechargeable sensor networks. We propose to utilize mobility for the joint problem of energy replenishment and data gathering. In particular, a multifunctional mobile entity, called SenCar, is employed to serve not only as a mobile 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 power transfer. Utilizing the control on SenCar’s mobility, we focus on the joint optimization on effective energy charging and high-performance data gathering. We first study this problem in generic networks with random topologies. A two-step approach for the joint design is proposed. In the first step, 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. To achieve a desirable balance between 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 bounded by a threshold. In the second step, we consider optimizing the data gathering performance when the SenCar migrates among these anchor points. We formulate this data gathering problem as 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. Besides the generic networks, we consider a special scenario as well, where sensors are regularly deployed. Correspondingly, we present a simplified solution with lower complexity by exploiting the symmetry of the topology. Finally, extensive evaluations are carried out to validate the effectiveness of the solutions. The results demonstrate that our approaches can maintain perpetual network operations and achieve high network utility on data gathering simultaneously.