Dynamic Trajectory Design and Bandwidth Adjustment for Energy-Efficient UAV-Assisted Relaying With Deep Reinforcement Learning in MEC IoT System

The use of Unmanned Aerial Vehicles (UAVs) is a promising solution for collecting data from wireless Internet of Things (IoT) devices and offloading to mobile edge computing (MEC) server embedded access points (APs) equipped with powerful servers. This paper presents a solution to optimize the energy efficiency of UAV relaying in the IoT system, which assists in programming the multiple UAV flight trajectories and bandwidth allocation schemes to scientifically and energy-efficiently transmit the data from IoT devices to MEC servers. Furthermore, in order to ensure the continuous and effective data relaying of the UAVs, we deploy a number of decentralized wireless charging stations (CSs) in the system to replenish the UAVs’ energy and enable them to provide long-term services. Specifically, we propose a deep reinforcement learning-based efficient IoT data relaying method, where we mainly apply deep deterministic policy gradient (DDPG) to solve this dynamic programming problem with large action spaces. Experimental results demonstrate that the DDPG-based method for UAV efficient data Collection and Offloading (DDPG-UCO) algorithm outperforms other five baseline methods in terms of the UAV energy efficiency, amount of data relayed and data interaction energy consumption rate while maintaining a high level of geographical fairness of the relaying service.