Digital Twin Traceability with DLT: Towards a Multi-Context and Universal Platform

Abstract

Digital twins (DTs) are transforming industries by offering real-time virtual representations of physical assets, enabling smarter decision-making and optimization. However, as these systems become more complex and distributed, maintaining reliable traceability remains a significant challenge. To address this, we propose a multi-context, modular platform that leverages blockchain and distributed ledger technologies (DLTs) to enhance the traceability of digital twins. Our proposal will ensure secure, immutable, and transparent records of data interactions, fostering greater trust, accountability, and interoperability across various domains. The foundation of this work involved identifying the key Architecturally Significant Requirements (ASRs) that must be considered in the platform's design. Based on the first ASRs related to traceability and data flexibility, we developed a conceptual architecture supported by an ontology that addresses the multi-context traceability challenge. The proposed approach was validated through two distinct case studies: livestock management and shop-floor processes. Moving forward, our focus will be on addressing the remaining ASRs that were already identified, to further develop a high-performance, secure, and scalable modular platform capable of supporting diverse contexts and applications.