Abstract
Digital twins (DTs) have emerged as a transformative paradigm for monitoring and optimizing physical systems, yet their real-time synchronization with distributed embedded systems (DES) remains a critical challenge. This paper proposes a novel framework that integrates clock synchronization protocols with predictive analytics to maintain high-fidelity digital twins under stringent timing constraints. The framework employs a hybrid approach combining GPS-based synchronization (Sterzbach, 1997) and self-stabilizing algorithms (Dolev, 1997) to achieve bounded clock skew, while a lightweight machine learning model predicts system states to compensate for synchronization delays. Experimental validation on a simulated DES testbed demonstrates that the proposed method reduces synchronization error by 40% compared to conventional timestamping and achieves prediction latency under 5 ms. The results highlight the feasibility of real-time DT synchronization for latency-sensitive applications such as smart manufacturing and autonomous vehicles. This work advances the state of the art by bridging deterministic synchronization mechanisms with data-driven predictive analytics, enabling mission-critical embedded systems to leverage digital twins without compromising real-time guarantees.
Keywords
digital twin, real-time synchronization, distributed embedded systems, predictive analytics, clock synchronization, machine learning, cyber-physical systems, IoT