Digital Twin-empowered Intelligent Computation Offloading for Edge Computing in the Era of 5G and Beyond: A State-of-the-Art Survey
Open accessAbstract
Edge computing has emerged as a promising paradigm for addressing the latency, bandwidth, and scalability challenges associated with traditional cloud-centric architectures. Computation offloading , the process of transferring computational tasks from edge devices to more powerful remote servers or cloud infrastructure, plays a crucial role in optimizing performance and resource utilization in edge computing systems. However, traditional computation offloading techniques often face limitations related to latency, network dependency, and scalability. In this survey, we explore the integration of digital twin (DT) technology into edge computing environments to empower intelligent computation offloading decisions. DTs, virtual representations of physical entities or systems that mirror their real-world counterparts, offer opportunities to enhance situational awareness , optimize resource allocation , and enable more informed decision-making at the edge. We provide a comprehensive overview of DTs empowered intelligent computation offloading, covering the fundamentals of DTs, traditional computation offloading techniques, and their limitations in edge computing. Additionally, we discuss how DTs can address these challenges and improve computation offloading strategies, along with practical applications and use cases across various domains. Finally, we identify open research challenges and opportunities for future exploration in this emerging field. Through this survey, we aim to provide researchers, practitioners, and stakeholders with insights into the potential of DTs to revolutionize computation offloading for edge computing and drive innovation in this rapidly evolving area.
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Recent work has emphasized predictive failure mitigation using Digital Twins for forecasting and planning.
“Recent work has emphasized predictive failure mitigation using Digital Twins for forecasting and planning [3,4,14,17,18].”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [14]
Traditional Digital Twins observe and model the system but do not themselves become the system.
“These twins observe and model the system but do not themselves become the system.”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [14]
The notion of a twin that exists not to model the system but to become the system upon failure remains unexplored.
“While model-based DTs have been extensively studied for forecasting, anomaly detection, and what-if analysis [3,4,14], the notion of a twin that exists not to model the system but to become the system upon failure remains unexplored.”
From Digital Twins at the Edge: A High-Availability Framework for Resilient Data Processing in IoT Sensor Networks (2026) · cites this paper as [14]
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Dang, H., Vo, H. M., Vo, V. T., Bui, D. A., & Bao, V. N. Q. (2026) · ICT Express · doi:10.1016/j.icte.2026.10.014
- Authors
- Tran-Dang, H., & Kim, D.-S.
- Affiliations
- Kumoh National Institute of Technology (KR)
- Corresponding author
- Kim, Dong-Seong
- ORCID
- Kim, Dong-Seong
- Venue
- ICT Express
- Publisher
- Elsevier BV
- Published
- 2025-02
- Volume
- 11
- Issue
- 1
- Pages
- 167-180
- ISSN
- 2405-9595 (ISSN-L)
- OpenAlex
- W4406291869
- Cited by
- 38
- References
- 68
- Citations by year
- 2026: 21 · 2025: 17
- FWCI
- 15.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- ICT Express (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- IITP
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- digital twin, computation offloading, edge computing, 5G, intelligent decision-making, resource allocation, latency, network dependencies, scalability, edge devices, cloud computing
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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.
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