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AuthorRen, Tao
AuthorNiu, Jianwei
AuthorDai, Bin
AuthorLiu, Xuefeng
AuthorHu, Zheyuan
AuthorXu, Mingliang
AuthorGuizani, Mohsen
Available date2022-10-09T09:59:50Z
Publication Date2022-05-15
Publication NameIEEE Internet of Things Journal
Identifierhttp://dx.doi.org/10.1109/JIOT.2021.3071531
CitationRen, T., Niu, J., Dai, B., Liu, X., Hu, Z., Xu, M., & Guizani, M. (2021). Enabling Efficient Scheduling in Large-Scale UAV-Assisted Mobile-Edge Computing via Hierarchical Reinforcement Learning. IEEE Internet of Things Journal, 9(10), 7095-7109.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103884869&origin=inward
URIhttp://hdl.handle.net/10576/34955
AbstractDue to the high maneuverability and flexibility, unmanned aerial vehicles (UAVs) have been considered as a promising paradigm to assist mobile edge computing (MEC) in many scenarios including disaster rescue and field operation. Most existing research focuses on the study of trajectory and computation-offloading scheduling for UAV-assisted MEC in stationary environments, and could face challenges in dynamic environments where the locations of UAVs and mobile devices (MDs) vary significantly. Some latest research attempts to develop scheduling policies for dynamic environments by means of reinforcement learning (RL). However, as these need to explore in high-dimensional state and action space, they may fail to cover in large-scale networks where multiple UAVs serve numerous MDs. To address this challenge, we leverage the idea of 'divide-and-conquer' and propose HT3O, a scalable scheduling approach for large-scale UAV-assisted MEC. First, HT3O is built with neural networks via deep RL to obtain real-time scheduling policies for MEC in dynamic environments. More importantly, to make HT3O more scalable, we decompose the scheduling problem into two-layered subproblems and optimize them alternately via hierarchical RL. This not only substantially reduces the complexity of each subproblem, but also improves the convergence efficiency. Experimental results show that HT3O can achieve promising performance improvements over state-of-the-art approaches.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectComputation offloading
Hierarchical reinforcement learning (HRL)
Mobile edge computing (MEC)
Trajectory optimization
Unmanned aerial vehicle
TitleEnabling Efficient Scheduling in Large-Scale UAV-Assisted Mobile-Edge Computing via Hierarchical Reinforcement Learning
TypeArticle
Pagination7095-7109
Issue Number10
Volume Number9
dc.accessType Abstract Only


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