Green Resource Allocation Based on Deep Reinforcement Learning in Content-Centric IoT

In the era of information, the green services of content-centric IoT are expected to offer users the better satisfaction of Quality of Experience (QoE) than that in a conventional IoT. Nevertheless, the network traffic and new demands from IoT users increase along with the promising of the content-c...

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Veröffentlicht in:	IEEE transactions on emerging topics in computing Jg. 8; H. 3; S. 781 - 796
Hauptverfasser:	He, Xiaoming, Wang, Kun, Huang, Huawei, Miyazaki, Toshiaki, Wang, Yixuan, Guo, Song
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.07.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Communications traffic Computation Computational modeling Computer simulation content-centric computing Deep learning deep reinforcement learning Electronic mail Green resource allocation Heuristic algorithms IoT Machine learning QoE Quality of experience Quality of service Resource allocation Resource management User satisfaction Wireless networks
ISSN:	2168-6750, 2168-6750
Online-Zugang:	Volltext
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