Edge intelligence empowered metaverse: architecture, technologies, and open issues

Xu, Y., Feng, D., Zhao, M., Sun, Y. and Xia, X.-G. (2023) Edge intelligence empowered metaverse: architecture, technologies, and open issues. IEEE Network, (doi: 10.1109/MNET.2023.3317477) (Early Online Publication)

Abstract

Recently, the metaverse has emerged as a focal point of widespread interest, capturing attention across various domains. However, the construction of a pluralistic, realistic, and shared digital world is still in its infancy. Due to the ultra-strict requirements in security, intelligence, and real-time, it is urgent to solve the technical challenges existed in building metaverse ecosystems, such as ensuring the provision of seamless communication and reliable computing services in the face of a dynamic and time-varying complex network environment. In terms of digital infrastructure, edge computing (EC), as a distributed computing paradigm, has the potential to guarantee computing power, bandwidth, and storage. Meanwhile, artificial intelligence (AI) is regarded as a powerful tool to provide technical support for automated and efficient decision-making for metaverse devices. In this context, this paper focuses on integrating EC and AI to facilitate the development of the metaverse, namely, the edge intelligence-empowered metaverse. Specifically, we first outline the metaverse architecture and driving technologies and discuss EC as a key component of the digital infrastructure for metaverse realization. Then, we elaborate on two mainstream classifications of edge intelligence in metaverse scenarios, including AI for edge and AI on edge. Finally, we identify some open issues.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Science and Technology Major Project under Grant 2020YFB1807601, the Guangdong Key Areas Research and Development Program under Grant 2022B0101010001, the Shenzhen Science and Technology Program under Grants JCYJ20210324095209025, the National Natural Science Foundation of China under Grant No.62361056, and the Applied Basic Research Foundation of Yunnan Province under Grant Nos. 202201AT070203 and 202301AT070422.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao
• Authors: Xu, Y., Feng, D., Zhao, M., Sun, Y., and Xia, X.-G.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Network
• Publisher: IEEE
• ISSN: 0890-8044
• ISSN (Online): 1558-156X
• Published Online: 06 October 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Network 2023
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher