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AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment: Challenges and Solutions

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. ISSN 0890-8044

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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
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: Elements
Depositing User: Elements
Date Deposited: 09 Feb 2022 14:55
Last Modified: 09 Feb 2022 14:59
URI: http://repository.essex.ac.uk/id/eprint/32236

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