Leveraging Target k-Coverage in Wireless Rechargeable Sensor Networks

Energy remains a major hurdle in running computation-intensive tasks on wireless sensors. Recent efforts have been made to employ a Mobile Charger (MC) to deliver wireless power to sensors, which provides a promising solution to the energy problem. Most of previous works in this area aim at maintaining perpetual network operation at the expense of high operating cost of MC. In the meanwhile, it is observed that due to low cost of wireless sensors, they are usually deployed at high density so there is abundant redundancy in their coverage in the network. For such networks, it is possible to take advantage of the redundancy to reduce the energy cost. In this paper, we relax the strictness of perpetual operation by allowing some sensors to temporarily run out of energy while still maintaining target k-coverage in the network at lower cost of MC. We first establish a theoretical model to analyze the performance improvements under this new strategy. Then we organize sensors into load-balanced clusters for target monitoring by a distributed algorithm. Next, we propose a charging algorithm named λ-GTSP Charging Algorithm to determine the optimal number of sensors to be charged in each cluster to maintain k-coverage in the network and derive the route for MC to charge them. We further generalize the algorithm to encompass mobile targets as well. Our extensive simulation results demonstrate significant improvements of network scalability and cost saving that MC can extend charging capability over 2-3 times with a reduction of 40% of moving cost without sacrificing the network performance.

I. Introduction and Related Work

Recent advances in technology are leading the trend to launch intelligent applications on smart phones, wireless sensors and wearable devices, and integrate them into the Internet-of-Things. These applications usually rely on ubiquitous sensors to capture and generate a huge amount of data from multi-dimensions to detect, recognize and classify objects/targets with high accuracy. However, energy consumption still remains a major challenge for wireless sensors to conduct intensive data processing, computation and communication. As an emerging technology, wireless charging has provided a convenient way to charge the battery of a sensor without wires or plugs. Sensors can be charged in distance by either deploying static wireless transmitters [1], [2] or Mobile Chargers (MCs) [4]–[7], [9], [10], [13].