Industrial Metaverse: Enabling Technologies, Open Problems, and Future Trends

TL;DR

The paper surveys the Industrial Metaverse, arguing that integrating Metaverse technologies into manufacturing can transform product design, production operations, quality inspection, and testing. It identifies six enabling technologies—Blockchain, Privacy-Preserving Computing, Digital Twin, 5G/6G, Extended Reality, and AI—and details their roles, current advances, and deployment stages in industrial contexts. It also outlines three core challenges—security/privacy, resource allocation, and interoperability—and reviews related standardization efforts (e.g., MPEG-V, IEEE 2888, ISO/IEC/IEEE 24765, MSF). Finally, it presents open issues and future directions, including flexible networks, privacy-preserving scalability, unified architectures, and integration with large-scale pre-trained and quantum computing, to guide future research and industrial adoption.

Abstract

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. This deployment, called the Industrial Metaverse, is used for product design, production operations, industrial quality inspection, and product testing. However, there lacks of in-depth understanding of the enabling technologies associated with the Industrial Metaverse. This encompasses both the precise industrial scenarios targeted by each technology and the potential migration of technologies developed in other domains to the industrial sector. Driven by this issue, in this article, we conduct a comprehensive survey of the state-of-the-art literature on the Industrial Metaverse. Specifically, we first analyze the advantages of the Metaverse for industrial production. Then, we review a collection of key enabling technologies of the Industrial Metaverse, including blockchain (BC), digital twin (DT), 6G, XR, and artificial intelligence (AI), and analyze how these technologies can support different aspects of industrial production. Subsequently, we present numerous formidable challenges encountered within the Industrial Metaverse, including confidentiality and security concerns, resource limitations, and interoperability constraints. Furthermore, we investigate the extant solutions devised to address them. Finally, we briefly outline several open issues and future research directions of the Industrial Metaverse.

Figures (9)

• Figure 2: Structure of the survey.
• Figure 3: Industrial Metaverse Architecture. It consists of three layers: the data input layer, the enabling layer, and the industrial application layer. The enabling layer includes six components: AI, DT, BC, XR, the Metaverse management center, and the data processing system. In this context, the data input layer is composed of various industrial IoT devices. The collected multimodal industrial data is transmitted to the data processing system for preprocessing. The processed data can be stored in the BC or streamed to the AI component for analysis. The results of data analysis are used to respond to requests from the Metaverse management center, and decision outcomes are fed back to the BC. The BC performs updates on the network and DT. The DT achieves proportional replication and synchronization of virtual and physical devices and transfers parameter information to XR for 3D modeling. XR displays decision solutions through human-machine interaction devices and is applied in various industrial scenarios. Among them, PPC ensures that data is not disclosed during processing and analysis.
• Figure 4: The vulnerabilities, advantages, and application stage of BC in Industrial Metaverse.
• Figure 5: The vulnerabilities, advantages, and application stage of PPC in Industrial Metaverse.
• Figure 6: The vulnerabilities, advantages, and application stage of DT in Industrial Metaverse.