Deep-Reinforcement-Learning-Based Computation Offloading for Servicing Dynamic Demand in Multi-UAV-Assisted IoT Network

In wireless networks, meeting the performance requirements of all tasks solely with Internet of Things (IoT) devices is challenging due to their limited computational power and battery capacity. Given their flexibility and mobility, the application of unmanned aerial vehicles (UAVs) in the context o...

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Veröffentlicht in:	IEEE internet of things journal Jg. 11; H. 10; S. 17249 - 17263
Hauptverfasser:	Lin, Na, Bai, Lu, Hawbani, Ammar, Guan, Yunchong, Mao, Chaojin, Liu, Zhi, Zhao, Liang
Format:	Journal Article
Sprache:	Englisch Japanisch
Veröffentlicht:	Piscataway IEEE 15.05.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Autonomous aerial vehicles Computation offloading Deep learning deep reinforcement learning (DRL) Dynamic scheduling Edge computing Energy consumption Greedy algorithms Heuristic algorithms Internet of Things Mixed integer Mobile computing mobile edge computing (MEC) Nonlinear programming Resource management Servers Task analysis Task scheduling unmanned aerial vehicle (UAV) Unmanned aerial vehicles Wireless networks
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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