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

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...

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Vydané v:	IEEE internet of things journal Ročník 9; číslo 5; s. 3899 - 3912
Hlavní autori:	Wang, Yang, Gao, Zhen, Zhang, Jun, Cao, Xianbin, Zheng, Dezhi, Gao, Yue, Ng, Derrick Wing Kwan, Renzo, Marco Di
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
ISSN:	2327-4662, 2372-2541, 2327-4662
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