Contents

Metaverse, an emerging concept, has attracted lots of attention worldwide recently. Exploiting core technologies such as 5G/6G communications, virtualization, AI, and blockchain, metaverse is expected to realize an immersive, embodied, and interoperable virtual world. As a result, the metaverse is believed to have the potential to play a transformative role in gaming, social networking, education, healthcare and more scenarios. However, a fundamental challenge, i.e., distributed intelligence for metaverse, has not been well studied. In the vision of metaverse, a huge number of AI-empowered edge devices, cloud servers and network equipment collaborate in creating and maintaining a shared virtual world. Such a collaboration is critical for robustness, efficiency and security of metaverse. Meanwhile, the interaction between smart devices and servers depends on the efficiency of data perception, transmission and processing in human motion, environment and virtual services. Collecting and tracking data from various terminal devices and conducting online intelligent computation and analysis of a large amount of data is necessary. However, existing distributed computing and networking techniques cannot well deal with these emerging challenges because they still heavily rely on human experiences and involvement. Therefore, research developments and breakthroughs in distributed intelligence will become the cornerstone of metaverse. We invite submissions on the research topic of Distributed Intelligence for Metaverse, covering both theoretical and systems studies.

• Emerging learning techniques for intelligent metaverse
• Federated learning for metaverse
• Merge of machine learning and SDN/NFV for metaverse
• Data collection and analytics for intelligent metaverse
• Content distribution networks and wireless caching for intelligent metaverse
• Human sensing and human computer interaction for metaverse
• Optimization of resource allocation and management for intelligent metaverse
• Resource provisioning and management for intelligent metaverse
• Energy efficiency, harvesting, and power control for intelligent metaverse
• Cross-Layer design for ultra-low latency metaverse applications
• Mobility issues in intelligent metaverse
• Edge/cloud computing for intelligent metaverse
• Network resilience, anomaly detection and reliability analysis for intelligent metaverse
• Security and privacy issues for intelligent metaverse
• Experimental prototyping and testbeds of intelligent metaverse
• New networks and communications architectures for intelligent metaverse
• Next-generation communications-enabled applications over intelligent metaverse

Submission Guide

Submitted papers must represent original material that is not currently under review in any other conference or journal and has not been previously published. Paper length should not exceed the 6-page standard IEEE conference two-column format (including all text, figures, and references). Please see the Author Information page for submission guidelines in the IEEE MetaCom 2024. All submitted papers will go through a peer review process. All accepted and presented papers will be included in the IEEE MetaCom 2024 proceedings. IEEE reserves the right to exclude an accepted and registered but not presented paper from the IEEE digital library. Please follow the submission link on https://metacom.cc track DIM) to submit your paper.

Organization Chairs

Peng Li (University of Aizu, Japan)
pengli@u-aizu.ac.jp

Kai Lin (Dalian University of Technology, P. R. China)
link@dlut.edu.cn

Jing Deng (UNC Greensboro, U.S.A)
jing.deng@uncg.edu

TPC Members

• Celimuge Wu, University of Electro-Communications, Japan
• Luan Hao, Xi'an Jiao Tong University, China
• Chao Fang, Beijing University of Technology, China
• Feng Ye, University of Dayton, US
• Kevin I-Kai Wang, University of Auckland, New Zealand
• Quan Chen, Shanghai Jiao Tong University, China
• Raylin Tso, National Chengchi University, Taiwan
• Soufiene Djahel, University of Huddersfield, UK
• Xiaoyan Wang, Ibaraki University, Japan
• Yufeng Zhan, Beijing Institute of Technology, China
• Zekeriya Erkin, Delft University of Technology, The Netherland