Distributed Multi-Agent Approach for Achieving Energy Efficiency and Computational Offloading in MECNs Using Asynchronous Advantage Actor-Critic

Mobile edge computing networks (MECNs) based on hierarchical cloud computing have the ability to provide abundant resources to support the next-generation internet of things (IoT) network, which relies on artificial intelligence (AI). To address the instantaneous service and computation demands of I...

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Bibliographic Details
Published in:Electronics (Basel) 2023-11, Vol.12 (22), p.4605
Main Authors: Khan, Israr, Raza, Salman, Khan, Razaullah, Rehman, Waheed ur, Rahman, G. M. Shafiqur, Tao, Xiaofeng
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Cloud computing
Collaboration
Communication
Computation offloading
Deep learning
Edge computing
Energy consumption
Energy efficiency
Internet of Things
Linear programming
Machine learning
Mathematical programming
Mixed integer
Mobile computing
Modal choice
Multi-agent systems
Multiagent systems
Optimization techniques
Resource allocation
Signal processing
Wireless networks
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Summary:Mobile edge computing networks (MECNs) based on hierarchical cloud computing have the ability to provide abundant resources to support the next-generation internet of things (IoT) network, which relies on artificial intelligence (AI). To address the instantaneous service and computation demands of IoT entities, AI-based solutions, particularly the deep reinforcement learning (DRL) strategy, have been intensively studied in both the academic and industrial fields. However, there are still many open challenges, namely, the lengthening convergence phenomena of the agent, network dynamics, resource diversity, and mode selection, which need to be tackled. A mixed integer non-linear fractional programming (MINLFP) problem is formulated to maximize computing and radio resources while maintaining quality of service (QoS) for every user’s equipment. We adopt the advanced asynchronous advantage actor-critic (A3C) approach to take full advantage of distributed multi-agent-based solutions for achieving energy efficiency in MECNs. The proposed approach, which employs A3C for computing offloading and resource allocation, is shown through numerical results to significantly reduce energy consumption and improve energy efficiency. This method’s effectiveness is further shown by comparing it to other benchmarks.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12224605