Engineering opportunistic digital twins with lingua franca

Digital Twins (DTs) have emerged as essential tools for virtualizing and enhancing Cyber-Physical Systems (CPS) by providing synchronized digital counterparts that enable monitoring, control, prediction, and optimization. Initially conceived as passive digital shadows, DTs are increasingly evolving into intelligent and proactive entities, enabled by the integration of Artificial Intelligence (AI). Among these advancements, Opportunistic Digital Twins (ODTs) represent a novel class of DTs: living, AI-aided, and actionable models that opportunistically exploit edge-cloud resources to deliver enriched and adaptive representations of physical entities and processes. However, despite their promise, current research lacks systematic engineering methods to ensure reliable coordination, determinism, and real-time responsiveness of ODTs in distributed and resource-constrained CPS. This article addresses this gap by introducing an engineering approach to build dependable and efficient ODTs by leveraging the deterministic concurrency, explicit timing semantics, and disciplined event handling of Lingua Franca (LF). The approach is exemplified through a Smart Traffic Management case study centered on Emergency Vehicle Preemption (EVP), where the ODT dynamically selects AI models based on runtime conditions while ensuring deterministic coordination across distributed nodes. Experimental results confirm the feasibility and effectiveness of our methodology, underscoring the potential of LF-based ODT engineering to enhance reliability, adaptability, and scalability in intelligent and distributed CPS deployments.