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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE internet of things journal Jg. 9; H. 5; S. 3899 - 3912
Hauptverfasser:	Wang, Yang, Gao, Zhen, Zhang, Jun, Cao, Xianbin, Zheng, Dezhi, Gao, Yue, Ng, Derrick Wing Kwan, Renzo, Marco Di
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.03.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!