Collaborative digital twin ecosystems

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Authors: Pasindu Kuruppuarachchi, Susan Rea, Bernd-Ludwig Wenning, Alan McGibney
Cited by: 4
References: 33

Abstract

• Collaborative Digital Twins Ecosystem • NFT base asset governance platform • Multi governance architecture Standalone Digital Twins (DTs) have demonstrated significant value in an increasingly digitalised world. However, collaboration among independent DTs and integration with supporting digital systems are required to maximise their full potential. Establishing a Collaborative Digital Twin Ecosystem (CDTE), incorporating systems such as enterprise resource planning and legacy systems, is essential for informed decision-making; yet, this remains challenging due to heterogeneity in implementations, domains, and standards. This study first classifies various DT types and examines their characteristics, and then compares leading DT reference architectures and security frameworks. Building on these insights, the previously proposed Collaborative Digital Twin Architecture (CDTA) is extended to incorporate NFT-based asset governance and dynamic policy enforcement via smart contracts. The enhanced CDTA is evaluated both qualitatively and quantitatively, considering its features and the value it provides to the CDTE. While policy enforcement introduces some computational overhead, it substantially improves trust, governance, and system-level security. Application-specific optimisation strategies, such as trust-based fast tracking, are proposed to mitigate these overheads. Compatibility with major reference architectures—including IIC, DTC, ISO, and RAMI 4.0—is validated, and integration with the IIC DT Security Maturity Model confirms the CDTA’s ability to support secure and scalable CDTEs across diverse domains, including smart cities, manufacturing, and critical infrastructure.

Fields of Study & Subjects

Digital Transformation in Industry Flexible and Reconfigurable Manufacturing Systems IoT and Edge/Fog Computing

Publication details

Journal / source
Future Generation Computer Systems
Publisher
Elsevier BV
Volume
181
Pages
108429–108429
Publication date
17 February 2026
Language
EN