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المؤلفRen, Tao
المؤلفNiu, Jianwei
المؤلفDai, Bin
المؤلفLiu, Xuefeng
المؤلفHu, Zheyuan
المؤلفXu, Mingliang
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-10-09T09:59:50Z
تاريخ النشر2022-05-15
اسم المنشورIEEE Internet of Things Journal
المعرّفhttp://dx.doi.org/10.1109/JIOT.2021.3071531
الاقتباسRen, 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.‏
معرّف المصادر الموحدhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85103884869&origin=inward
معرّف المصادر الموحدhttp://hdl.handle.net/10576/34955
الملخصDue 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعComputation offloading
Hierarchical reinforcement learning (HRL)
Mobile edge computing (MEC)
Trajectory optimization
Unmanned aerial vehicle
العنوانEnabling Efficient Scheduling in Large-Scale UAV-Assisted Mobile-Edge Computing via Hierarchical Reinforcement Learning
النوعArticle
الصفحات7095-7109
رقم العدد10
رقم المجلد9
dc.accessType Abstract Only


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