A survey on digital twin: definitions, characteristics, applications, and design implications
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When, in 1956, Artificial Intelligence (AI) was officially declared a research field, no one would have ever predicted the huge influence and impact its description, prediction, and prescription capabilities were going to have on our daily lives. In parallel to continuous advances in AI, the past decade has seen the spread of broadband and ubiquitous connectivity, (embedded) sensors collecting descriptive high dimensional data, and improvements in big data processing techniques and cloud computing. The joint usage of such technologies has led to the creation of digital twins, artificial intelligent virtual replicas of physical systems. Digital Twin (DT) technology is nowadays being developed and commercialized to optimize several manufacturing and aviation processes, while in the healthcare and medicine fields this technology is still at its early development stage. This paper presents the results of a study focused on the analysis of the state-of-the-art definitions of DT, the investigation of the main characteristics that a DT should possess, and the exploration of the domains in which DT applications are currently being developed. The design implications derived from the study are then presented: they focus on socio-technical design aspects and DT lifecycle. Open issues and challenges that require to be addressed in the future are finally discussed.
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Grieves' proposed model has three components: Real Space, Virtual Space, and interlinking mechanisms for data/information transfer enabling convergence and synchronization.
“The three components of Grieves' proposed model include the Real Space, the Virtual Space, and the interlinking mechanisms between these two for transferring data/information enabling seamless convergence and synchronization between them [25].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [25]
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A Semiotic Approach to EUD for the Internet of Things.
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Sorted by citations
Tao, F., Xiao, B., Qi, Q., Cheng, J., & Ji, P. (2022) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2022.06.015 · Cited by 1,255 · see citing works
Digital twin: origin to future
Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D. (2021) · Applied System Innovation · doi:10.3390/asi4020036 · Cited by 1,038 · see citing works
Pham, Q.-V., Nguyen, D. C., Huynh-The, T., Hwang, W.-J., & Pathirana, P. N. (2020) · IEEE Access · doi:10.1109/access.2020.3009328 · Cited by 413 · see citing works
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 · see citing works
Human disease models in drug development
Loewa, A., Feng, J. J., & Hedtrich, S. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00063-3 · Cited by 331 · see citing works
Human Digital Twin for Fitness Management
Barricelli, B. R., Casiraghi, E., Gliozzo, J., Petrini, A., & Valtolina, S. (2020) · IEEE Access · doi:10.1109/access.2020.2971576 · Cited by 259 · see citing works
Self-citationDigital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case
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Digital twins in infrastructure: definitions, current practices, challenges and strategies
Broo, D. G., & Schooling, J. (2023) · International Journal of Construction Management · doi:10.1080/15623599.2021.1966980 · Cited by 184 · see citing works
Zeb, S., Mahmood, A., Hassan, S. A., Piran, M. J., Gidlund, M., & Guizani, M. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2021.103309 · Cited by 132 · see citing works
Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions
Getu, T. M., Kaddoum, G., & Bennis, M. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3520707 · Cited by 110 · see citing works
Digital Twins for Healthcare Using Wearables
Johnson, Z., & Saikia, M. J. (2024) · Bioengineering · doi:10.3390/bioengineering11060606 · Cited by 74 · see citing works
Digital twin systems for musculoskeletal applications: A current concepts review
Diniz, P., Grimm, B., Garcia, F., Fayad, J., Ley, C., Mouton, C., Oeding, J. F., Hirschmann, M. T., Samuelsson, K., & Seil, R. (2025) · Knee Surgery, Sports Traumatology, Arthroscopy · doi:10.1002/ksa.12627 · Cited by 60 · see citing works
Optimal Placement of Social Digital Twins in Edge IoT Networks
Chukhno, O., Chukhno, N., Araniti, G., Campolo, C., Iera, A., & Molinaro, A. (2020) · Sensors · doi:10.3390/s20216181 · Cited by 42 · see citing works
Cyber-physical systems in water management and governance
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Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0
Mon, B. F., Hayajneh, M., Ali, N. A., Ullah, F., Ullah, H., & Alkobaisi, S. (2025) · Computers, Materials & Continua · doi:10.32604/cmc.2025.061411 · Cited by 26 · see citing works
“The three components of Grieves’ proposed model include the Real Space , the Virtual Space , and the interlinking mechanisms between these two for transferring data/information enabling seamless convergence and synchronization between them [25].”
Alkhodari, M., Xiong, Z., Khandoker, A. H., Hadjileontiadis, L. J., Leeson, P., & Lapidaire, W. (2023) · Expert Review of Cardiovascular Therapy · doi:10.1080/14779072.2023.2223978 · Cited by 23 · see citing works
Human Digital Twin-based interactive dashboards for informal caregivers of stroke patients
Lauer-Schmaltz, M. W., Kerim, I., Hansen, J. P., Gulyás, G. M., & Andersen, H. B. (2023) · Proceedings of the 16th International Conference on PErvasive Technologies Related to Assistive Environments · doi:10.1145/3594806.3594824 · Cited by 18 · see citing works
- Authors
- Barricelli, B. R., Casiraghi, E., & Fogli, D.
- Affiliations
- University of Brescia (IT)
- University of Milan (IT)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 167653-167671
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2988359971
- Cited by
- 1,480
- References
- 108
- Citations by year
- 2026: 221 · 2025: 281 · 2024: 306 · 2023: 295 · 2022: 230 · 2021: 117 · 2020: 29
- FWCI
- 69.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Institutional Research Information System (Università degli Studi di Brescia) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Applied Advanced Technologies
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- digital twin, digital twin applications, socio-technical design, artificial intelligence, big data, cloud computing, sensor data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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