Jiang, Feibo and Wang, Kezhi and Dong, Li and Pan, Cunhua and Xu, Wei and Yang, Kun (2021) AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment: Challenges and Solutions. IEEE Network, 35 (1). pp. 400-408. DOI https://doi.org/10.1109/mnet.011.2000440
Jiang, Feibo and Wang, Kezhi and Dong, Li and Pan, Cunhua and Xu, Wei and Yang, Kun (2021) AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment: Challenges and Solutions. IEEE Network, 35 (1). pp. 400-408. DOI https://doi.org/10.1109/mnet.011.2000440
Jiang, Feibo and Wang, Kezhi and Dong, Li and Pan, Cunhua and Xu, Wei and Yang, Kun (2021) AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment: Challenges and Solutions. IEEE Network, 35 (1). pp. 400-408. DOI https://doi.org/10.1109/mnet.011.2000440
Abstract
By taking full advantage of Computing, Communication and Caching (3C) resources at the network edge, Mobile Edge Computing (MEC) is envisioned as one of the key enablers for next generation networks. However, current fixed-lo-cation MEC architecture may not be able to make real-time decision in dynamic environments, especially in large-scale scenarios. To address this issue, in this article, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, i.e., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions, are discussed. In addition, the AI-based joint Resource schEduling (ARE) framework with two different AI-based mechanisms, i.e., Deep neural network (DNN)-based and deep reinforcement learning (DRL)-based architectures, are proposed. DNN-based solution with online incremental learning applies the global optimizer and therefore has better performance than the DRL-based architecture with online policy updating, but requires longer training time. The simulation results are given to verify the efficiency of our proposed ARE framework.
Item Type: | Article |
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Uncontrolled Keywords: | Artificial intelligence; Computer architecture; Task analysis; Real-time systems; Training; Vehicle dynamics; Resource management |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 09 Feb 2022 14:55 |
Last Modified: | 30 Oct 2024 16:34 |
URI: | http://repository.essex.ac.uk/id/eprint/32236 |
Available files
Filename: 2002.05020.pdf