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| Title | Future Trends And Research Challenges In Digital Twins |
|---|---|
| ID_Doc | 27552 |
| Authors | Kameswari Y.L.; Omkar Lakshmi Jagan B.; Mohammed T.K.; Abdel Aleem S.H.E. |
| Year | 2024 |
| Published | Digital Twins for Smart Cities and Villages |
| DOI | http://dx.doi.org/10.1016/B978-0-443-28884-5.00004-X |
| Abstract | With applications ranging from manufacturing and healthcare to urban planning and energy, digital twin technology has become a ground-breaking idea. In order to provide light on the potential breakthroughs and obstacles that lie ahead, this study examines future trends and research challenges in the field of digital twins. Several significant themes are anticipated to influence the development of digital twins as they continue to change. Digital twins will be better able to learn from complex situations in real time because to the combination of artificial intelligence (AI) and machine learning (ML). Digital twins will be given the ability to foresee, improve, and react to dynamic changes as a result of this AI-driven evolution, which will ultimately result in more effective and resilient systems. Another important development is the idea of federated digital twins, in which various interconnected digital twin instances work together to represent a larger, interconnected system. By integrating the strengths of several digital twins, this method makes it easier to model and analyze very complex and interconnected systems, such as smart cities or multimodal transportation networks. It is also projected that digital twins would spread into the Internet of Things (IoT) space. A closer connection between the real and virtual worlds will be made possible by the seamless integration of sensors, actuators, and data streams with digital twin platforms. This pattern will open the door to fresh perspectives and opportunities for improvement. To fully realize the potential of digital twins, a number of scientific challenges must be overcome. As the integration of real-time data from physical systems raises worries about unauthorized access and potential vulnerabilities, data privacy and security continue to be of the utmost importance. Additionally, sophisticated methods for data assimilation, model validation, and uncertainty quantification are needed to create accurate and trustworthy digital twin models. Another urgent issue is interoperability. The creation of standardized interfaces and protocols is essential to facilitate seamless integration and data sharing as digital twins proliferate across many sectors and domains. Furthermore, novel approaches to distributed computing and high-performance simulation are necessary to meet the scalability and computational requirements of large-scale digital twin ecosystems. In-depth analysis of these trends and problems is provided in this chapter, along with suggestions for future research areas and solutions. The field of digital twins is set for a paradigm-shifting impact on how we build, function, and interact with the physical world by tackling these issues and exploiting new trends. © 2025 Elsevier Inc. All rights reserved. |
| Author Keywords | Artificial intelligence; Digital twins; Integration; Internet of Things; Machine learning; Urban planning |
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