Trajectory Design for UAV-Based Internet of Things Data Collection: A Deep Reinforcement Learning Approach

• Published in: IEEE internet of things journal Vol. 9; no. 5; pp. 3899 - 3912
• Main Authors: Wang, Yang, Gao, Zhen, Zhang, Jun, Cao, Xianbin, Zheng, Dezhi, Gao, Yue, Ng, Derrick Wing Kwan, Renzo, Marco Di
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Completion time; Data collection; Deep learning; deep reinforcement learning (DRL); Engineering Sciences; Internet of Things; Internet of Things (IoT); Machine learning; Markov processes; Minimization; Nodes; Optimization; Resource management; Sensors; Signal and Image processing; Three-dimensional displays; Trajectory; trajectory design; Trajectory optimization; unmanned aerial vehicle (UAV) communications; Unmanned aerial vehicles; Urban environments; Internet-of-Things (IoT); trajectory design; deep reinforcement learning; UAV communications; data collection

In this article, we investigate an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) system in a sophisticated 3-D environment, where the UAV's trajectory is optimized to efficiently collect data from multiple IoT ground nodes. Unlike existing approaches focusing only on a simplif...