GUEST EDITORS’ INTRODUCTION

Special Issue on Digital Twins
Ziran Wang , Purdue University, West Lafayette, IN, 47907, USA
Michael Grieves , Digital Twin Institute Cocoa Beach, FL, 32931, USA
Yonggang Wen , Nanyang Technological University, 639798, Singapore
Fan Xue , The University of Hong Kong, Pokfulam, Hong Kong

This issue features eight articles that explore diverse aspects of digital twin (DT)
research. Several works focus on the foundational frameworks and computational
advancements of DTs: The first article presents a scalable cloud-native platform
for deploying universal DTs, while the second introduces a multifidelity data fusion
(MDF) mechanism for integrating real-time Internet of Things (IoT) sensor data
with historical simulation models. Other contributions emphasize industrial and
infrastructure applications: The third article develops a generative modeling
approach for DT-based shop floor management, and the fourth investigates DTenabled location-based services for smart buildings. In the realm of sustainability
and energy optimization, the fifth article proposes a cognitive DT for microgrid
energy management, and the sixth applies DT technology to cultural heritage
preservation, specifically for the Great Wall of China. Finally, two articles highlight
emerging trends: The seventh explores human DTs (HDTs) to enhance interactions
within intelligent ecosystems, and the eighth presents the Internet of Twins
framework as a scalable DT platform for diverse industrial applications. Together,
these contributions showcase the evolving capabilities of DTs and their expanding
role in bridging the physical and digital worlds.

D

Ts have emerged as a transformative technology, revolutionizing industries by providing a dynamic digital counterpart to physical
systems. By leveraging real-time data integration,
simulation, and artificial intelligence, DTs enable
predictive maintenance, system optimization, and
enhanced decision making across sectors, such as
manufacturing, health care, energy, and transportation. The increasing convergence of IoT, cloud
computing, and machine learning has significantly
expanded the capabilities of DTs, allowing for more
sophisticated, scalable, and adaptive implementations. As organizations strive for greater efficiency

1089-7801 © 2025 IEEE. All rights reserved, including rights
for text and data mining, and training of artificial intelligence
and similar technologies.
Digital Object Identifier 10.1109/MIC.2025.3540532
Date of current version 7 March 2025.
January/February 2025

and resilience, DTs are becoming a key enabler of
next-generation intelligent systems.
Despite their growing adoption, several challenges hinder the full realization of DT potential, including
real-time data processing, interoperability, security, and scalability. Addressing these challenges requires innovative frameworks, robust architectures,
and interdisciplinary collaboration. This special
issue explores recent advancements in DT technology, showcasing state-of-the-art research that
enhances system intelligence, operational efficiency, and human–machine collaboration. The articles
included in this issue provide valuable insights into
the latest computational models, application-driven
implementations, and emerging trends shaping the
evolution of DTs. We hope this special issue inspires
further research and innovation in the field of DTs,
paving the way for smarter, more autonomous, and
highly optimized digital–physical systems.

Published by the IEEE Computer Society

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GUEST EDITORS’ INTRODUCTION

APPENDIX: RELATED
ARTICLES
A1. T. Häußermann, J. Lehmann, A. Rache, F. Kolb,
F. Wühler, and J. Reichwald, “UniTwin: Pushing
universal digital twins into the clouds through
reconfigurable container environments,” IEEE
Internet Comput., vol. 29, no. 1, pp. 8–15,
Jan./Feb. 2025, doi: 10.1109/MIC.2024.3489876.
A2. H. Wang, X. Song, and C. Zhang, “Multifidelity
data fusion mechanism for digital twins via the
Internet of Things,” IEEE Internet Comput., vol.
29, no. 1, pp. 16–23, Jan./Feb. 2025, doi: 10.1109/
MIC.2024.3483831.
A3. Y. Wu, Y. Sun, X. Wen, X. Liu, J. Bao, and S. Wang, “A
generative modeling method for digital twin shop
floor,” IEEE Internet Comput., vol. 29, no. 1,
pp. 24–31, Jan./Feb. 2025, doi: 10.1109/
MIC.2024.3522301.
A4. A. Chambon, A. Rachedi, A. Sahli, and A. Mebarki,
“From IoT networks deployment to robust
location-based services using the digital twin
of a building,” IEEE Internet Comput., vol. 29,

IN THIS ISSUE
The special issue presents eight high-quality articles
that explore the latest advancements in DT technology across diverse domains, including industrial applications, urban planning, energy management, and
human-centered digital ecosystems. Below, we summarize each article’s contributions and impact.
The first articleA1 presents UniTwin, a cloud-native framework for deploying and managing universal
DTs in containerized environments. The framework is
designed to address scalability, modularity, and domain-independence challenges through a composite
design pattern and Kubernetes-based orchestration.
The study demonstrates UniTwin’s effectiveness in supporting scalable DT management across different application domains, paving the way for broader adoption
of DT technology in cloud computing environments.
The second articleA2 introduces an MDF mechanism
for DTs that integrates real-time IoT sensor data with
historical finite element method simulation results. The
authors propose the improved hierarchical regression
for the MDF method, which enhances prediction accuracy by leveraging both high-fidelity and low-fidelity
data sources. Numerical experiments validate the approach, demonstrating its effectiveness in maintaining
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A5.

A6.

A7.

A8.

no. 1, pp. 32–38, Jan./Feb. 2025, doi: 10.1109/
MIC.2024.3502631.
B. Sivaneasan, K. T. Tan, and W. Zhang, “Cognitive
digital twins for the microgrid: A real-world
study for intelligent energy management and
optimization,” IEEE Internet Comput., vol. 29,
no. 1, pp. 39–47, Jan./Feb. 2025, doi: 10.1109/
MIC.2024.3488896.
Z. Zhang, A. Dang, J. Huang, and Y. Chen,
“Advancing conservation methods of the great
wall cultural heritage through digital twin,” IEEE
Internet Comput., vol. 29, no. 1, pp. 48–55,
Jan./Feb. 2025, doi: 10.1109/MIC.2025.3539367.
S. Laso, J. L. Herrera, J. Galán-Jiménez, and
J. Berrocal, “Human digital twins: Enhancing
interactions with digital ecosystems,” IEEE
Internet Comput., vol. 29, no. 1, pp. 56–64,
Jan./Feb. 2025, doi: 10.1109/MIC.2024.3509672.
L. V. Cakir, M. Özdem, H. Ahmadi, T. Q. Duong, and
B. Canberk, “Internet of twins approach: Digitaltwin-as-a-platform architecture,” IEEE Internet
Comput., vol. 29, no. 1, pp. 65–74, Jan./Feb. 2025,
doi: 10.1109/MIC.2024.3491915.

a low normalized root mean square error and computational efficiency. The proposed mechanism offers significant potential for improving DT applications across
industrial and smart infrastructure domains.
The third articleA3 presents a novel generative modeling approach for DT-based shop floor management,
leveraging large language models and ontology-based
information models. The proposed framework extracts domain knowledge dynamically, enabling
real-time construction and semantic enrichment of
DT shop floor models. The study validates the method
through a case study on shop floor resource scheduling, demonstrating improved efficiency in model generation and industrial applicability.
The fourth articleA4 investigates the integration of
DTs with IoT networks to optimize location-based services in smart buildings. The authors propose a novel
framework that enhances IoT deployment strategies
through genetic algorithms and a multitier architecture,
improving coverage and extending service availability.
A use case on indoor guidance services demonstrates
the system’s potential to enhance operational efficiency and user experience in smart environments.
The fifth articleA5 presents a cognitive DT for microgrid optimization developed for a real-world deployment in Singapore. The DT framework includes a
January/February 2025

GUEST EDITORS’ INTRODUCTION

client for real-time control and a cloud-based server
for computationally intensive energy management algorithms. The system is validated through operational
scenarios, demonstrating significant improvements
in microgrid resilience, efficiency, and cost reduction,
with only a 0.24% difference from the theoretical optimal energy cost.
The sixth articleA6 explores the use of DT technology
for cultural heritage conservation, focusing on the Beichakou section of the Great Wall in China. The authors
propose the “Beichakou Method,” which integrates real-time monitoring, risk assessment, and conservation
planning through a multitier DT framework. The study
highlights the benefits of DTs in optimizing conservation efforts while identifying challenges in data acquisition, multiparty coordination, and system scalability.
The seventhA7 article explores the concept of HDTs
and their role in enhancing interactions within digital
ecosystems. The authors propose a multicontext distributed HDT architecture that integrates human-specific data with smart systems, addressing challenges
in privacy, interoperability, and real-time adaptation.
The study outlines key application areas, including Industry 5.0 and intelligent transportation, and
highlights the potential for HDTs to improve human–
system collaboration.

The eighth articleA8 introduces the Internet of
Twins concept, proposing a DT as a platform architecture for scalable and interoperable DT deployments.
By leveraging a knowledge graph-based orchestration
approach, the framework enhances data integration
and system automation. A pilot study in wind energy management demonstrates the system’s ability to
optimize fault detection, reduce data preprocessing
overhead, and improve operational efficiency.
ZIRAN WANG is an assistant professor at Purdue University,
West Lafayette, IN, 47907, USA. Contact him at ziran@ieee.org.
MICHAEL GRIEVES is executive director of the Digital
Twin Institute, Cocoa Beach, FL, 32931, USA. Contact him at
mgrieves@ameritech.net.
YONGGANG WEN is a professor at Nanyang Technological
University, 639798, Singapore. Contact him at ygwen@ntu.
edu.sg.
FAN XUE is an associate professor at The University of Hong
Kong, Pokfulam, Hong Kong. Contact him at xuef@hku.hk.

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