Digital twins are sophisticated software systems for the representation, monitoring, and control of cyber-physical systems, including automotive, avionics, smart manufacturing, and many more. Existing definitions and reference models of digital twins are overly abstract, impeding their comprehensive understanding and implementation guidance. Consequently, a significant gap emerges between abstract concepts and their industrial implementations. We analyze popular reference models for digital twins and combine these into a significantly detailed unifying reference model for digital twins that reduces the concept-implementation gap to facilitate their engineering in industrial practice. This enhances the understanding of the concepts of digital twins and their relationships and guides developers to implement digital twins effectively.

In manufacturing, digital twins, realized as Asset Administration Shells (AAS), have emerged as a prevalent practice. These digital replicas, often utilized as structured repositories of asset-related data, facilitate interoperability across diverse systems. However, extant approaches treat the AAS as a static information model, lacking support for dynamic service integration and system adaptation. The existing body of literature has not yet thoroughly explored the potential for integrating executable behavior, particularly in the form of containerized services, into or from the AAS. This integration could serve to enable proactive functionality. In this paper, we propose a submodel-based architecture that introduces a structured service notion to the AAS, enabling services to dynamically interact with and adapt AAS instances at runtime. This concept is implemented through the extension of a submodel with behavioral definitions, resulting in a modular event-driven architecture capable of deploying containerized services based on embedded trigger conditions. The approach is illustrated through a case study on a 3-axis milling machine. Our contribution enables the AAS to serve not only as a passive digital representation but also as an active interface for executing added-value services.

Several software architectures for digital twins have been proposed, specifying their structure and key components, such as the ISO 23247 standard for manufacturing. However, systematic methodologies for developing digital twins in the manufacturing domain remain lacking. Existing research does not adequately address methodological aspects, such as composing essential digital twin components, defining their interfaces, and systematically analyzing relevant data. This gap presents a challenge for the structured development of digital twins. In this work, we introduce a systematic approach to identify key requirements for manufacturing digital twins and propose a model-driven method for their creation. Our approach enables the generation of executable digital twins based on a formalized specification. We demonstrate its applicability through multiple industrial manufacturing demonstrators, highlighting its suitability in practice. The proposed method is adaptable regarding the purpose of the digital twins to be developed and supports a largely automated process.

With the evolution of communication systems toward the sixth‐generation technology (6G), intelligent cognitive communication has gained considerable attention. As an important part of intelligent cognitive communication, cognitive radio (CR) offers promising prospects for efficient spectrum utilization. However, with the introduction of cognitive capabilities, CR networks (CRNs) face not only common security threats in wireless systems, but also unique security threats, including primary user emulation (PUE) attacks, endangering communication reliability and confidentiality. In order to enhance the defense ability of CRNs against PUE attacks, this paper proposes an integrated sensing and communication (ISAC)‐assisted approach. Leveraging ISAC technology, our scheme enhances location detection precision. We introduce a high‐resolution perception signal parameter estimation method and a position‐based identity authentication scheme. Furthermore, deep reinforcement learning is used to dynamically optimize the authentication threshold to ensure the stability of authentication in dynamic scenarios. Simulation results show that the proposed scheme is effective in resisting PUE attacks and improves the security and reliability of CRNs.

Abstract Digital twins (DTs) are proliferating in a multitude of domains, including agriculture, automotive, avionics, logistics, manufacturing, medicine, smart homes, etc. As domain experts and software experts both have to contribute to the engineering of effective DTs, several model-driven engineering (MDE) approaches have been recently proposed to ease the design, development, and operation of DTs. However, the diversity of domains in which MDE is currently applied to DTs, as well as the diverse landscape of DTs and MDE applications to DTs, makes it challenging for researchers and practitioners to get an overview of what techniques and artifacts are already applied in this context. In this paper, we shed light on the aforementioned aspects by performing a systematic mapping study on the application of MDE automation techniques, i.e., model-to-model transformation, code generation, and model interpretation, in the context of DTs as well as on the characteristics of DTs including the twinned systems to which these techniques are applied in different domains. We systematically retrieved a set of 189 unique publications, of which 66 were selected for further investigation in this paper. Our results indicate that the distribution of employed MDE techniques (136 applications of automation techniques) is balanced between the different techniques, but there are significant variations for different DT types. With respect to the different domains, we found that even though applications are available in many domains, a small number of domains currently dominate applications of MDE to DTs, i.e., more than half of included papers are in the manufacturing and transportation domains.

Abstract Engineering digital twins is a software and systems engineering challenge for which no systematic approach exists. The Asset Administration Shell is becoming a popular foundation for digital twins in Industry 4.0 and it comes in different types that support the engineering of different kinds and parts of digital twins. We investigate how it supports realizing common requirements for digital twins. To this end, we investigate how each of the three Asset Administration Shell types can contribute to the systematic engineering of specific components of digital twins. Therefore, we analyzed popular definitions and conceptual models of digital twins and extracted requirements that at least two of them share. We compare the resulting requirements with Asset Administration Shells of different types and conclude with open challenges in the implementation of digital twins with this technology. This supports practitioners and researchers in identifying the most suitable type of Asset Administration Shell for their specific digital twin engineering needs and identifies gaps worthy of future research toward a systematic engineering of digital twins.

Cyber-Physical Production Systems necessitate efficient configuration and continuous reconfiguration to adapt to evolving requirements and shifting goals. This process involves integrating various system structures to derive new functions and behaviors. The concept of the Digital Twin, which allows for effective integration and testing, is instrumental in facilitating this task. However, a fundamental prerequisite is that Digital Twins of individual system components must be composed efficiently and in accordance with dynamic System-of-Systems. We address this challenge by introducing an approach for the automated horizontal and vertical composition of Digital Twins, aimed at minimizing manual intervention and enhancing adaptability. Therefore, a pipeline specifically designed for this goal is proposed, which includes the generation of new functions and behaviors. This approach is intended to provide a foundation for future research.

Control software for production systems is typically developed by domain experts, despite its high complexity. The increasingly available Large Language Models (LLMs) can assist developers with code generation and debugging. However, their suitability for generating control software for production systems is still unexplored. Therefore, this study explores the generation of Structured Text (ST) according to IEC-61131-3 by different LLMs. We selected 21 coding examples that are representative of PLC programming and developed an approach for comparing the outputs of different LLMs using metrics for testing generated code (CodeBERTScore, pass@k, generation time). The strategies for prompt optimization that were developed as part of this work can be directly used for improved ST generation. Our results show that, at the time of the study, ChatGPT-4 had the highest reliability in generating syntactically correct ST code that expresses the desired functionality.

Modern industrial systems, characterised by distributed and fragmented equipment, present challenges due to their inherent heterogeneity and complexity. This should not impact the stakeholders' business logic, who are more concerned with the information itself rather than how it is collected or processed. Recently, Digital Twins - software copies of physical assets and systems - emerged as a pivotal strategy to bridge the cyber-physical world into an effective digital layer decoupling applications from the management and interaction with physical assets. Fostering this vision, we propose a structured industrial Digital Twins ecosystem exploiting twin relationships and hier-archies to build a digitalised replica of the whole manufacturing system structure enabling a simplified navigation and interaction with the physical world and the data it generates. To support the depicted visions, a fully functioning prototype has been implemented and evaluated in an experimental scenario.