Digital Twin in Industries: A Comprehensive Survey

TL;DR

This comprehensive survey investigates Digital Twin (DT) technology as a unifying framework linking physical industrial systems with high-fidelity virtual models. It systematically covers DT fundamentals, components, and enabling technologies (edge computing, ML, and wireless networks), then dissects DT services (data sharing, offloading, ISAC, caching, resource allocation, wireless networking, and metaverse) and maps them to a wide range of industrial domains (manufacturing, healthcare, transportation, energy, agriculture, space, oil & gas, robotics). A detailed security and privacy analysis spans physical, digital, and HMI layers with a suite of countermeasures and governance guidance, complemented by taxonomy tables and a synthesis of key findings and future directions. The paper highlights DT’s potential to enhance real-time decision-making, predictive maintenance, and operational resilience, while underscoring challenges in data governance, interoperability, and security across Industry 4.0 contexts. Overall, it provides a foundational reference for researchers and practitioners aiming to deploy DTs at scale in next-generation industrial networks, including 6G, big data, standardization, and quantum-enabled security trajectories.

Abstract

Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.

Figures (9)

• Figure 1: The convergence of DT and industries.
• Figure 2: Organization of this article.
• Figure 3: Dynamic task offloading for digital-twin empowered mobile edge computing networks chen2023dynamic.
• Figure 4: DT-empowered vehicular social edge network zhang2021digital. Enabled by bidirectional communications between vehicles and virtual entities, DT offers content caching services to support vehicular network management.
• Figure 5: Summary of DT application domains presented in this paper.