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Maximizing the Promptness of Metaverse Systems Using Edge Computing by Deep Reinforcement Learning

Year: 2024

Type: conference-paper

Abstract: Metaverse and Digital Twin (DT) have attracted much academic and industrial attraction to approach the future digital world. This paper introduces the advantages of deep reinforcement learning (DRL) in assisting Metaverse system-based Digital Twin. In this system, we assume that it includes several Metaverse User devices collecting data from the real world to transfer it into the virtual world, a Metaverse Virtual Access Point (MVAP) undertaking the processing of data, and an edge computing server that receives the offloading data from the MVAP. The proposed model works under a dynamic environment with various parameters changing over time. The experiment results show that our proposed DRL algorithm is suitable for offloading tasks to ensure the promptness of DT in a dynamic environment.

Source: International Conference on Advanced Technologies for Communications

DOI: 10.1109/atc63255.2024.10908145

Authors: Tam Ninh Thi-Thanh, Trinh van Chien, Hung Tran, Nguyen Hoai Son, Van Nhan Vo

Institutions: Trường Đại học Khoa học và Công nghệ Hà Nội, Hanoi University of Science and Technology, Phenikaa University, Vietnam National University, Hanoi, VNU University of Engineering and Technology, Duy Tan University

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W4408199688

FWCI:

Cites: 10

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Related to: 10