Special Issue on Digital Twins
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 DT-enabled 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.
Authors
- Ziran Wang (ORCID: https://orcid.org/0000-0003-2702-7150)
- Yonggang Wen (ORCID: https://orcid.org/0000-0002-2751-5114)
- Michael Grieves (ORCID: https://orcid.org/0000-0003-0322-343X)
- Fan Xue (ORCID: https://orcid.org/0000-0003-2217-3693)
Institutions
- Nanyang Technological University (SG)
- Purdue University West Lafayette (US)
- University of Hong Kong (HK)
Publication Details
- Journal
- IEEE Internet Computing
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/mic.2025.3540532
- Citations
- 1
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 1.24