Industrial Metaverse: Enabling Technologies, Open Problems, and Future Trends
As an emerging technology that enables seamless integration between the physical and virtual worlds, the Metaverse has great potential to be deployed in the industrial production field with the development of extended reality (XR) and next-generation communication networks. The Industrial Metaverse is used for product design, production operations, quality inspection, and testing. However, there is limited understanding of the enabling technologies associated with it, including the specific industrial scenarios targeted by each technology and the potential migration of technologies from other domains to the industrial sector. This paper provides a comprehensive survey of the latest literature on the Industrial Metaverse. We first analyze its advantages for industrial production, then review key enabling technologies such as blockchain (BC), privacy-preserving computing (PPC), digital twin (DT), fifth/sixth generation mobile communication technology (5G/6G), XR, and artificial intelligence (AI), and explore how these technologies support different aspects of industrial production. We also present major challenges in the Industrial Metaverse, including privacy and security concerns, resource limitations, and interoperability constraints, along with existing solutions. Finally, we outline several open issues and future research directions.
Authors
- Zhu Han
- Ming Ding (ORCID: https://orcid.org/0000-0002-3690-0321)
- Dinh C. Nguyen (ORCID: https://orcid.org/0000-0002-8092-6756)
- Long Shi (ORCID: https://orcid.org/0000-0001-6124-5173)
- Wen Chen (ORCID: https://orcid.org/0000-0003-2133-8679)
- Jun Li (ORCID: https://orcid.org/0000-0002-6239-2922)
- Shiying Zhang (ORCID: https://orcid.org/0009-0003-8976-3847)
Institutions
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Shanghai Jiao Tong University (CN)
- Nanjing University of Science and Technology (CN)
- Data61 (AU)
- University of Houston (US)
- Southeast University (CN)
- University of Alabama in Huntsville (US)
Publication Details
- Journal
- IEEE Communications Surveys & Tutorials
- Published
- 2025-04-24
- DOI
- https://doi.org/10.1109/comst.2025.3563919
- Citations
- 16
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 19.88
Funders
- National Natural Science Foundation of China