Deploying complex Cyber-Physical Systems (CPSs) is challenging due to the gap between abstract design models and their physical implementation. This often requires manual recoding, an error-prone process that breaks the continuity from a verified model to the final deployed system. To bridge this gap, this paper introduces a methodology that enables a direct and seamless transition from a formal computational model to its physical deployment, eliminating the need for manual recoding. The core aim is to use a single, unmodified model for both simulation and real-world operation. We propose a Model-Based Systems Engineering (MBSE) methodology grounded in the Discrete Event System Specification (DEVS) formalism. Its key innovation is the formalization of the Digital Twin (DT) concept as a reusable, executable DEVS coupled model, which explicitly structures the interface between the system’s digital logic and its physical counterpart. The methodology is implemented using the xDEVS simulation engine, whose Real-Time (RT) capabilities and built-in hardware protocol handlers (e.g., Inter-Integrated Circuit (I 2 C), MQTT) allow the formal model to directly control physical components. We demonstrated the methodology by adapting the purely computational DEVS-BLOOM model to a physical emulation controlling a small-scale Unmanned Surface Vehicle (USV). Field tests confirmed the physical USV, operated by the unmodified DEVS model running in real-time, successfully performed its autonomous navigation and monitoring mission. This successful validation is demonstrated using this single-case study as a foundational proof-of-concept. Our approach provides a robust and seamless pathway from a verified computational model to a reliable real-world system. With the formalization of the physical–digital interface inside the model itself, the methodology effectively closes the abstraction–implementation gap in CPS development.

This paper presents a new algorithm for the Discrete EVent System Specification (DEVS) formalism that improves the performance of simulating complex systems by reducing the number of iterations through the model components in each simulation step. It also minimizes unnecessary visits to model components by propagating simulation routines only when necessary. Additionally, we provide two parallel versions of this new simulation algorithm that use work-stealing scheduling and avoid locking mechanisms without compromising the validity of the execution in shared-memory architectures. We implemented the proposed algorithms in the xDEVS simulator and evaluated their performance using the DEVStone synthetic benchmark. The results show that the proposed algorithms outperform state-of-the-art alternatives. For computationally intensive models, parallel implementations achieve high parallelism efficiency. Furthermore, they are more resilient to model complexity than the sequential algorithm, showing better performance for complex models even without computational overhead in state transition functions.

Abstract The advent of edge computing holds immense promise for advancing the digitization of society, ushering in critical applications that elevate the overall quality of life. Yet, the practical implementation of the edge paradigm proves more challenging than anticipated, encountering disruptions primarily due to the constraints of applying conventional cloud‐based strategies at the network's periphery. Increasingly influenced by sustainability commitments, industry regulations currently view edge computing as a potential threat, primarily due to the energy inefficiency of solutions situated in close proximity to data generation sources and the rising density of computing. This paper presents a proactive strategy to transform the perceived threat into an opportunity, steering the sustainable evolution of future edge infrastructures to make them both environmentally and economically competitive for accelerated adoption. The vision outlined addresses key challenges associated with edge deployment and operation, emphasizing energy efficiency, fault‐tolerant automation, and collaborative orchestration. The proposed approach integrates two‐phase immersion cooling, formal modeling, machine learning, and federated management to effectively harness heterogeneity, propelling the sustainability of edge computing. To substantiate the efficacy of this approach, the paper details initial efforts towards establishing the sustainability of an edge infrastructure designed for an Advanced Driver Assistance Systems application.

Abstract Employing Modeling and Simulation (M&S) extensively to analyze and develop complex systems is the norm today. The use of robust M&S formalisms and rigorous methodologies is essential to deal with complexity. Among them, the Discrete Event System Specification (DEVS) provides a solid framework for modeling structural, behavior and information aspects of any complex system. This gives several advantages to analyze and design complex systems: completeness, verifiability, extensibility, and maintainability. DEVS formalism has been implemented in many programming languages and executable on multiple platforms. In this paper, we describe the features of an M&S framework called xDEVS that builds upon the prevalent DEVS Application Programming Interface (API) for both modeling and simulation layers, promoting interoperability between the existing platform‐specific (C++, Java, Python) DEVS implementations. Additionally, the framework can simulate the same model using sequential, parallel, or distributed architectures. The M&S engine has been reinforced with several strategies to improve performance, as well as tools to perform model analysis and verification. Finally, xDEVS also facilitates systems engineers to apply the vision of model‐based systems engineering (MBSE), model‐driven engineering (MDE), and model‐driven systems engineering (MDSE) paradigms. We highlight the features of the proposed xDEVS framework with multiple examples and case studies illustrating the rigor and diversity of application domains it can support.