The DOI matches its Crossref record; 159 of the 159 references it cites were found in a registry or an index.
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects
Open accessAbstract
Digital Twin (DT) is an emerging technology surrounded by many promises, and potentials to reshape the future of industries and society overall. A DT is a system-of-systems which goes far beyond the traditional computer-based simulations and analysis. It is a replication of all the elements, processes, dynamics, and firmware of a physical system into a digital counterpart. The two systems (physical and digital) exist side by side, sharing all the inputs and operations using real-time data communications and information transfer. With the incorporation of Internet of Things (IoT), Artificial Intelligence (AI), 3D models, next generation mobile communications (5G/6G), Augmented Reality (AR), Virtual Reality (VR), distributed computing, Transfer Learning (TL), and electronic sensors, the digital/virtual counterpart of the real-world system is able to provide seamless monitoring, analysis, evaluation and predictions. The DT offers a platform for the testing and analysing of complex systems, which would be impossible in traditional simulations and modular evaluations. However, the development of this technology faces many challenges including the complexities in effective communication and data accumulation, data unavailability to train Machine Learning (ML) models, lack of processing power to support high fidelity twins, the high need for interdisciplinary collaboration, and the absence of standardized development methodologies and validation measures. Being in the early stages of development, DTs lack sufficient documentation. In this context, this survey paper aims to cover the important aspects in realization of the technology. The key enabling technologies, challenges and prospects of DTs are highlighted. The paper provides a deep insight into the technology, lists design goals and objectives, highlights design challenges and limitations across industries, discusses research and commercial developments, provides its applications and use cases, offers case studies in industry, infrastructure and healthcare, lists main service providers and stakeholders, and covers developments to date, as well as viable research dimensions for future developments in DTs.
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Cites
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567 · cited 1×
“…inference are probable and therefore an epistemic limitation; (2) traceability between the input data and conclusion is not accessible and open to critique; (2) conclusions are dependent upon the quality of data; or (4) the actions based on conclusions are discriminatory even if well-founded [191].”
BackgroundAgent-based modeling: Methods and techniques for simulating human systems
Bonabeau, E. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082080899 · Cited by 4,500
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391
Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems
Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727 · cited 1×
“The original slide containing the proposed concept was reproduced by Grieves and Vickers in [4] and it can be noted that the early architecture of what would later become the DT consisted of three main components: • the real space , • the virtual space , • the link serving as a communication medium…”
MethodDigital twin: Enabling technologies, challenges and open research
Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020) · IEEE Access · doi:10.1109/access.2020.2998358 · Cited by 2,965
6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies
Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019) · IEEE Vehicular Technology Magazine · doi:10.1109/mvt.2019.2921208 · Cited by 2,575
Digital twin: values, challenges and enablers from a modeling perspective
Rasheed, A., San, O., & Kvamsdal, T. (2020) · IEEE Access · doi:10.1109/access.2020.2970143 · Cited by 1,952
Enabling technologies and tools for digital twin
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579
Tao, F., Qi, Q., Wang, L., & Nee, A. Y. C. (2019) · Engineering · doi:10.1016/j.eng.2019.01.014 · Cited by 1,487
Cited by
Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263909 · Cited by 357
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304
Walia, G. K., Kumar, M., & Gill, S. S. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3338015 · Cited by 279
Modern computing: Vision and challenges
Gill, S. S., Wu, H., Patros, P., Ottaviani, C., Arora, P., Pujol, V. C., Haunschild, D., Parlikad, A. K., Cetinkaya, O., Lutfiyya, H., et al. (2024) · Telematics and Informatics Reports · doi:10.1016/j.teler.2024.100116 · Cited by 197
A comprehensive review of Digital Twin technologies in smart cities
Huzzat, A., Anpalagan, A., Khwaja, A. S., Woungang, I., Alnoman, A. A., & Pillai, A. S. (2025) · Digital Engineering · doi:10.1016/j.dte.2025.100040 · Cited by 65
Metadata
- Authors
- Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R. V., Venkataraman, H., Trestian, R., & Nguyen, H. X.
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2022
- Volume
- 24
- Issue
- 4
- Pages
- 2255-2291
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W4296913260
- Cited by
- 1,049
- References
- 159
- FWCI
- 89.86
- Type
- journal article
- Language
- en
- Open access
- green
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- Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R. V., Venkataraman, H., Trestian, R., & Nguyen, H. X. (2022). Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects. IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/comst.2022.3208773
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
