Wireless Rechargeable Sensor Network (WRSN) is largely used in monitoring of environment and traffic, video surveillance and medical care, etc., and helps to improve the quality of urban life. However, it is challenging to provide the sustainable energy for sensors deployed in buildings, soil or other places, where it is hard to harvest the energy from environment. To address this issue, we design a new wireless charging system, which levers the bus network assisted drone in urban areas. We formulate the drone scheduling problem based on this new wireless charging system to minimize the total time cost of drone subject to all sensors can be charged under the energy constraint of drone. Then, we propose an approximation algorithm DSA for the energy tightened drone scheduling problem. To make the tasks of WRSN sustainable, we further formulate the drone scheduling problem with deadlines of sensors, and present the approximation algorithm DDSA to find the drone schedule with the maximal number of sensors charged by the drone before deadlines. Through the extensive simulations, we demonstrate that DSA can reduce the total time cost by 84.83% compared with Greedy Replenished Energy algorithm, and uses at most 5.98 times of the total time cost of optimal solution on average. Then, we also demonstrate that DDSA can increase the survival rate of sensors by 51.95% compared with Deadline Greedy Replenished Energy algorithm, and can obtain 77.54% survival rate of optimal solution on average.

无线充电传感器网络（WRSN）广泛用于环境和交通监控，视频监控和医疗保健等，并有助于改善城市生活质量。但是，为部署在难以从环境中收集能量的建筑物，土壤或其他地方的传感器提供可持续能源具有挑战性。为了解决这个问题，我们设计了一种新的无线充电系统，该系统利用了市区内的公交网络辅助无人机。我们基于这种新的无线充电系统制定了无人机调度问题，以使无人机的总时间成本最小化，因为所有传感器都可以在无人机的能量约束下进行充电。然后，我们提出一种近似算法DSA针对能源紧张的无人机调度问题。为了使WRSN的任务可持续，我们进一步制定了带有传感器截止日期的无人机调度问题，并提出了近似算法DDSA来找到在截止日期之前无人机充电的传感器数量最大的无人机调度。通过广泛的仿真，我们证明，与贪婪补充能源算法相比，DSA可以将总时间成本减少84.83％，并且平均使用最优解决方案的总时间成本最多为5.98倍。然后，我们还证明DDSA与“最后期限贪婪补充能源”相比，可以将传感器的生存率提高51.95％ 算法，平均可以获得最优解的77.54％的生存率。