Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in SAGIN

In this article, we investigate a computing task scheduling problem in space-air-ground integrated network (SAGIN) for delay-oriented Internet of Things (IoT) services. In the considered scenario, an unmanned aerial vehicle (UAV) collects computing tasks from IoT devices and then makes online offloa...

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Veröffentlicht in:	IEEE transactions on wireless communications Jg. 20; H. 2; S. 911 - 925
Hauptverfasser:	Zhou, Conghao, Wu, Wen, He, Hongli, Yang, Peng, Lyu, Feng, Cheng, Nan, Shen, Xuemin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computation Computer aided scheduling constrained MDP Constraints Delay Delays edge computing Energy consumption Heuristic algorithms Internet of Things IoT Machine learning Markov processes Optimization Processor scheduling reinforcement learning Risk Scheduling Space-air-ground integrated network Task analysis Task scheduling Unmanned aerial vehicles US Department of Transportation
ISSN:	1536-1276, 1558-2248
Online-Zugang:	Volltext
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