Energy-Efficient Multidimensional Trajectory of UAV-Aided IoT Networks With Reinforcement Learning

Abstract

This article proposes a multidimensional search space (or directional space) with more degrees-of-freedom (DOFs) to increase the energy efficiency of limited-battery-powered unmanned aerial vehicle (UAV) in the Internet of Things (IoT) data collection scenario. In this article, the UAV navigates from the initial to the goal point while collecting data from IoT sensors on the ground. Owing to the limited battery power of UAVs, an optimized trajectory is a crucial practical problem. Based on the available directional space, the direction of the UAV related to the navigation trajectory is optimized using reinforcement learning (RL). The objective of RL is to maximize the energy efficiency of the UAV as a long-term reward by selecting the optimal direction. Moreover, a practical energy consumption model and environment are presented in this article. Simulation results verified that the proposed multidimensional trajectory for UAV achieves higher energy efficiency compared with benchmark models.