In a rechargeable wireless sensor network, utilizing the unmanned aerial vehicle (UAV) as a mobile base station (BS) to charge sensors and collect data effectively prolongs the network’s lifetime. In this paper, we jointly optimize the UAV’s flight trajectory and the sensor selection and operation modes to maximize the average data traffic of all sensors within a wireless sensor network (WSN) during finite UAV’s flight time, while ensuring the energy required for each sensor by wireless power transfer (WPT). We consider a practical scenario, where the UAV has no prior knowledge of sensor locations. The UAV performs autonomous navigation based on the status information obtained within the coverage area, which is modeled as a Markov decision process (MDP). The deep Q-network (DQN) is employed to execute the navigation based on the UAV position, the battery level state, channel conditions and current data traffic of sensors within the UAV’s coverage area. Our simulation results demonstrate that the DQN algorithm significantly improves the network performance in terms of the average data traffic and trajectory design.

With the rapid development of the mobile internet and the massive deployment of the Internet of Things, mobile devices, including both the consumer electronics and the sensors, become hungrier for the energy than ever before. Conventional cable based charging largely restrict the movement of the mobile devices. Wireless charging hence emerges as an essential technique for enabling our ultimate goal of charging anytime and anywhere. By efficiently exploiting the legacy of the existing communication infrastructure, we propose a novel data and energy integrated network (DEIN) in order to realise the radio frequency (RF) based wireless charging without degrading the information transmission. In this treatise, we focus on the implementation of the DEIN in both the theoretical and practical aspects, concerning the transceiver architecture design and the rectifier circuit design. Furthermore, we also present a Wi-Fi based testbed for demonstrating the availability of the RF based wireless charging.