Digital Twin in Industries: A Comprehensive Survey
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 25 of its 205 references that are in the map. First posted on arXiv 29 Nov 2024.
Builds on
25 of 205 references.
References · 25
- 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
- 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
- 2022Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunitiescited by 40
- 2021Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directionscited by 38
- 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
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- 2020Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networkscited by 15
- 2024Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Conceptcited by 15
- 2022Digital Twin for Networking: A Data-driven Performance Modeling Perspectivecited by 10
13 more
- 2023Privacy Attacks and Defenses for Digital Twin Migrations in Vehicular Metaversescited by 10
- 2024Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challengescited by 10
- 2020Machine learning based digital twin for dynamical systems with multiple time-scalescited by 9
- 2024Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networkscited by 8
- 2021Networked Twins and Twins of Networks: an Overview on the Relationship Between Digital Twins and 6Gcited by 5
- 2022Integrating Digital Twin and Advanced Intelligent Technologies to Realize the Metaversecited by 5
- 2022Bi-directional Digital Twin and Edge Computing in the Metaversecited by 5
- 2023Semantic-aware Digital Twin for Metaverse: A Comprehensive Reviewcited by 5
- 2023Adaptive Digital Twin for UAV-Assisted Integrated Sensing, Communication, and Computation Networkscited by 4
- 2023Digital Twin-Empowered Communications: A New Frontier of Wireless Networkscited by 3
- 2024Joint Vehicle Connection and Beamforming Optimization in Digital Twin Assisted Integrated Sensing and Communication Vehicular Networkscited by 3
- 2024Integrated Sensing and Communication Driven Digital Twin for Intelligent Machine Networkcited by 2
- 2024AI-enabled Cyber-Physical In-Orbit Factory -- AI approaches based on digital twin technology for robotic small satellite productioncited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025Spiking Neural Networks: The Future of Brain-Inspired Computingcited by 2
- 2026GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistantcited by 1
- 2026A Scalable Digital Twin Framework for Energy Optimization in Data Centerscited by 0
- 2026A Hybrid Edge Cloud Digital Twin for Welfare-Constrained Control in Poultry Productioncited by 0
- 2026A Hybrid Edge Cloud Digital Twin for Welfare-Constrained Control in Poultry Production18 Jun 2026
- 2026A Scalable Digital Twin Framework for Energy Optimization in Data Centers7 May 2026
- 2026GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant1 Mar 2026
- 2025Spiking Neural Networks: The Future of Brain-Inspired Computing31 Oct 2025