Stanford Database Library of American Journal of Applied Science and Technology

Vol. 6 No. 02 (2026)
Articles

Architecting Intelligent Digital Twin Ecosystems: Integrating Iot, Artificial Intelligence, And Edge Computing for Next-Generation Cyber-Physical Systems

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  • Serine Vettel
Serine Vettel
Department of Information Systems and Digital Engineering, University of Budapest, Hungary

Published 2026-02-28

Keywords

  • Digital Twin Systems,
  • Cyber-Physical Systems,
  • Internet of Things,
  • Artificial Intelligence Integration

How to Cite

Serine Vettel. (2026). Architecting Intelligent Digital Twin Ecosystems: Integrating Iot, Artificial Intelligence, And Edge Computing for Next-Generation Cyber-Physical Systems. Stanford Database Library of American Journal of Applied Science and Technology, 6(02), 117–122. Retrieved from http://oscarpubhouse.com/index.php/sdlajast/article/view/1078

Copyright (c) 2026 Serine Vettel

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The rapid evolution of cyber-physical systems, smart manufacturing environments, and intelligent infrastructures has significantly transformed the technological landscape of modern industries. Among the most transformative innovations emerging from this technological shift is the concept of the digital twin, which refers to a dynamic digital representation of physical systems that continuously synchronizes with real-world processes through real-time data streams. Digital twins have become central to the realization of Industry 4.0, enabling advanced analytics, predictive maintenance, real-time monitoring, and intelligent automation across diverse sectors such as manufacturing, supply chain management, agriculture, healthcare, and urban infrastructure. The integration of digital twins with enabling technologies including the Internet of Things, artificial intelligence, edge computing, and big data analytics has further expanded their capabilities, allowing organizations to build adaptive and intelligent systems capable of responding autonomously to changing operational conditions.

This research article investigates the architectural foundations, technological enablers, and cross-domain applications of digital twin ecosystems within contemporary cyber-physical environments. Drawing upon extensive literature across manufacturing systems, industrial informatics, communication technologies, and smart infrastructure, the study provides a comprehensive theoretical examination of how digital twin technologies are designed, deployed, and operationalized within modern digital ecosystems. Particular emphasis is placed on the role of IoT infrastructures in facilitating real-time data acquisition, the integration of artificial intelligence for predictive modeling and anomaly detection, and the importance of standardized data architectures in supporting scalable digital twin frameworks.

The study adopts a qualitative analytical methodology based on an extensive synthesis of academic literature and technological frameworks to examine emerging patterns in digital twin implementation. Findings indicate that digital twins are evolving from isolated simulation models toward interconnected platforms that form the backbone of intelligent cyber-physical ecosystems. However, significant challenges remain regarding interoperability, data governance, computational scalability, and security.

The article concludes by highlighting future research directions focusing on cross-domain digital twin interoperability, intelligent edge-driven architectures, and the development of global standards capable of supporting large-scale digital twin deployments in next-generation communication networks.

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