Joint Optimization of Data Acquisition and Trajectory Planning for UAV-Assisted Wireless Powered Internet of Things

The development of Internet of Things (IoT) technology has led to the emergence of a large number of Intelligent Sensing Devices (ISDs). Since their limited physical sizes constrain the battery capacity, wireless powered IoT networks assisted by Unmanned Aerial Vehicles (UAVs) for energy transfer and data acquisition have attracted great interest. In this paper, we formulate an optimization problem to maximize system energy efficiency while satisfying the constraints of UAV mobility and safety, ISD quality of service and task completion time. The formulated problem is constructed as a Constrained Markov Decision Process (CMDP) model, and a Multi-agent Constrained Deep Reinforcement Learning (MCDRL) algorithm is proposed to learn the optimal UAV movement policy. In addition, an ISD-UAV connection assignment algorithm is designed to manage the connection in the UAV sensing range. Finally, performance evaluations and analysis based on real-world data demonstrate the superiority of our solution.

I. Introduction

Due to the large-scale promotion and application of the Internet of Things (IoT) technology, a variety of rich IoT devices are springing up [1]. It is predicted that by 2030, Intelligent Sensing Devices (ISDs) will reach 500 billion units more [2]. However, such anytime and anywhere, a large number of different wireless network connections also put tremendous pressure to the traditional terrestrial communication infrastructure, requiring not only high data transmission rates, but also large sensing ranges. In scenarios of data sensing, the effectiveness and timeliness of collected data become very important, such as health data monitoring for patients with chronic diseases in telemedicine, temperature and humidity monitoring of crop growth environment in agricultural production [3], and electrical fault monitoring of power lines and power towers in smart grids [4]. In these scenarios, the data generated in the corresponding ISDs needs to be collected for further processing and analysis, and thus effective data acquisition can increase the accuracy of derived decisions. However, the limited battery capacity of each ISD only supports its operation for a short period, and a low-powered ISD may lead to large transmission delay in data uploading, which has become a key constraint for realizing sustainable communications in the IoT.