Technical interoperability is the first and foremost requirement for the Industrial Internet of Things. It ensures connectivity and networking between heterogeneous devices and systems, enabling their collaboration across different network protocols, both locally and over the Internet. Devices designed for industrial use are well known for their robustness, precision, and high-quality finish; however, they typically come with one or a few fixed industrial network protocols. One traditional method of connecting two devices using different protocols is to use a converter that translates messages between the two network interfaces. This paper proposes a unified protocol translation method for multiple network interfaces. The method is validated in a product assembly line use case, in which the involved devices use four different industrial Ethernet protocols and frameworks: Modbus TCP, EtherNet/IP Class 1, OPC UA PubSub, and ROS 2. Moreover, the correctness of such a complex networking system is verified using a runtime verification approach grounded in a formal interaction model, ensuring that the observed communication behaviors conform to the expected specification.

Industry 4.0, involving technological advances such as the Internet of Things, artificial intelligence, and autonomous robotics, promises to bring great benefits and outstanding innovations to traditional manufacturing. To make this vision a reality, one key challenge consists in harmonizing and adapting these new technologies to the industrial environment. However, there are two major obstacles. First, the technologies, divided into information technologies (IT) and operational technologies (OT), are not interoperable by default, making the IT and OT convergence a complex and uneasy problem. Second, deploying an Industry 4.0 system requires the participation of experts from different domains who have different technical vocabularies and knowledge. Therefore, communication and collaboration between these experts can be complicated, and every misunderstanding may cause the failure of a part or the whole system. Regarding the mentioned obstacles, this paper shares our successful experiences and approaches in an industrial waste management case study of the project OTPaaS. In detail, we have applied standardized modeling approaches, more specifically, the asset administration shell (AAS) digital twins models, for the interoperability between IT and OT involved in the system and used model-driven engineering (MDE) techniques to automate the translation of data models between the different stakeholders in the nroiect.

Digital Twin, the technological advance that has emerged in the Industry 4.0 era, will become one of the six technological pillars forming Industry 5.0, according to the vision of the European Commission. This promotion relies on the fact that digital-twin technologies are getting more mature and widely adopted in both the public and private sectors. Besides the progressions, a new challenge has arisen: harmonizing multiple digital twins in a single digital twin system. Indeed, digital twins built on different technologies addressing different aspects of an asset are not interoperable by default, making the interaction between them uneasy. Moreover, as an asset must have a connection with each digital twin for data exchange, its multiple connections with multiple digital twins may cause the asset itself and the involved network infrastructures to be overloaded. Regarding the above challenge, this paper proposes an approach based on the Asset Administration Shell (AAS) standard to harmonize multiple digital twins as a single digital twin with multiple facets.

Companies are increasingly needed to exchange data about their products and processes via a common secured and trusted network, in which they can self-determine who and how their data will be accessed and used. To address this growing need, we propose in this paper a model-based engineering approach that integrates the standards of the domain: the Asset Administration Shell (AAS) for creating data models representing the company's assets, ODRL for the specification of the data usage policies and the Eclipse Dataspace Connector (EDC) for International Data Spaces (IDS) compliant data exchange.

As digital twins gain momentum in their usage in diverse domains, the concept of Asset Administration Shells (AAS) has become very relevant for achieving the digital twin approach, where Administration Shells are the digital representation of physical assets. Being a relatively new concept in the Industrial Internet of Things (IIoT) domain, the tools and approaches for creating and deploying AASs are likewise in infancy. This paper introduces an open-source tool, Papyrus4Manufacturing, which provides a model-based systems engineering approach to the AAS. This toolset supports the creation of AAS digital twins from modeling to automatic deployment and connection to assets using the OPC UA protocol. This paper also includes an evaluation of its usability, as it is put to test with an academic use case.

Visual programming (VP) is increasingly used for educational purposes thanks to its user-friendly nature. However, there is also a clear need to take into consideration the teacher objectives and the different students needs. In fact, in practice, students have different levels, some of them have some difficulties, and they are also progressing differently. To deal with all these constraints, we propose "Studio4Education" (S4E), a novel web and desktop based visual programming environment for the development and deployment of IoT applications. Moreover, we integrated S4E with a model driven workbench (Eclipse Papyrus) where the teacher specifies an IoT problem for the students using a high level modeling language: Systems Modeling Language (SysML). Then he generates a configuration for S4E from the model to adapt the S4E VP interface to his goals and to the students level. We also demonstrate a seamless integration between these two tools using the Arrowhead framework.

Open Platform Communication Unified Architecture (OPC UA) has emerged as a highly-demanded standard in building industrial systems. One factor in this success is the concepts of OPC UA address space and OPC UA information model. OPC UA address space provides a mechanism to represent the resources of an OPC UA server and its field devices as OPC UA nodes. OPC UA information model structures OPC UA nodes as a schema. Once other devices and systems understand the schema, they can interact appropriately with the industrial system. Thus, designing the OPC UA information model is an undeniable and essential practice of OPC UA-based industrial system engineering. However, both academia and industry suffer from a lack of shared experiences on this subject. This gap motivated us to share our experiences on the development of an OPC UA information model. These experiences are based on an actual product assembly line monitoring case study developed at CEA LIST. Overall, in this paper, we aim to throw some light on the steps involved in the transformation of a requirement specification into an OPC UA information model.

In the opc UA standard, the opc UA information model is the key to semantic interoperability. It refers to an organized structure representing resources in the form of opc UA nodes residing in an opc UA address space. Before runtime, an opc UA server requires an opc UA information model already filled with opc UA nodes corresponding to the available resources of its field devices. It is tricky to the Industrial Internet of Things, a scenario where random field devices can join a sys-tem at any time. Indeed, the opc UA server needs to dynamically discover such devices and update its opc UA information model with new relevant opc UA nodes. Regarding the above challenge, this paper introduces a Web-of-Things (WoT) approach that enables field devices to register with an opc UA server to join the system without manual configuration. The approach relies on WoT Discovery and WoT Thing Description

Lot-size-one systems as well as plug and produce concepts imply (1) producing increased variety of products in a highly flexible and timely manner, and (2) making commissioning and maintenance more flexible. The speed with which manufacturers, in particular SMEs, can reconfigure the production to a new run and thus respond to clients and avoid costly machine downtime is critical to maintaining commercial success and profit margins. The manufacturing systems of tomorrow must offer a high degree of autonomy, be quickly re-planned to other operations, and cope with a wide variety of unforeseen situations, in a secure and safe manner. In this context, the Asset Administration Shell (AAS) is an emergent standard that leverages the digital twin approach and provides concepts for describing capabilities and skills of I4.0 components in order to automate the reconfiguration process. This article proposes a capability-based operation and engineering approach to tackle the syntactic and semantic interoperability problems in flexible production lines. We demonstrate the implementation of the AAS standard in the open source model-driven workbench Papyrus; then we assess its usability for modeling a production cell use case in order to implement a capability-based reconfiguration approach for flexible production lines.

The future intelligent manufacturing systems should possess a high degree of autonomy, which is able to monitor the entire production process, quickly re-plan operations, and respond to various unforeseen situations in a secure and safe manner. This can achieve rapid response to customers and avoid costly machine downtime, which is crucial to maintaining business success and profitability. The Asset Administration Shell (AAS) is an emerging standard in the I4.0 (Industry 4.0) domain. Based on the concept of digital twins, it provides concepts for describing the digital representation of I4.0 assets including their capabilities and skills. The AAS provides also responses to the challenge of syntactic and semantic interoperability that the flexible and autonomous production lines are facing. In this article, we propose a capability-based operation and engineering approach for flexible production lines. Our approach is relying on the AAS standard which is a very wide and rich specification. Consequently, we describe the subset of AAS modelling concepts necessary for our approach, we clarify their semantics and we show their usage through a production cell use case. Furthermore, we demonstrate how these modelling concepts were tooled as an extension of the open source model-driven workbench Papyrus.

In this paper, we propose a method and associate platform to couple model-based system engineering and safety analysis at the early phases of robotic system (RS) life-cycle. The method is compatible with IEC12100 and ISO13482. The platform is based on Papyrus UML modeler and supports RobotML, a domain specific language for RSs, as well as tools for safety analysis and risk assessment, Sophia and Safety Architect. It includes an ability (a) to model architecture of RSs; (b) to automatically run safety analysis (e.g. failure mode and effects analysis, fault tree analysis, etc.); (c) to save and reuse safety artefacts; (d) to represent safety analysis results in the modeling environment. We illustrate the proposed method by considering a humanoid personal care robot from SoftBank Robotics developed in the scope of the ROMEO2 project.

The complexity and intelligence of energy systems has increased in the recent years, whereas using Multi-agents system (MAS) technology has been recommended by IEEE for developing software solutions for modeling, controlling, and simulating their behaviors. Available MAS solutions for energy systems are generally designed for resolving specific problems by proposing ad-hoc solutions, without considering interoperability and reusability. We propose a methodology, based on the Model-Driven Engineering (MDE) technique, for developing MAS solutions for energy systems. Our methodology uses the Common Information Model standard (CIM), recommended by IEEE, and the existing Platform Independent agent metamodel PIM4Agents. Our proposal allows modeling MAS solutions for power engineering applications, by means of a platform independent model that abstracts developers from existing agent-oriented methodologies and platforms. Applying model transformations, the generated models can be transformed and executed within several agent platforms such as JACK and JADE. The proposal has been partially validated by means of a well-known test case.

Industry 4.0 is one of the most prominent initiatives towards the vision of smart manufacturing to foster efficiency and synergy among suppliers, producers, and customers. It will transform the production systems into a smart, fully integrated, and optimized cyber-physical production systems (CPPS) based on technology enablers such as Internet of Things (IoT). Moreover, the availability of a wide range of data and information in the context of CPPS provides a huge possibility to create novel business opportunities. However, for these business opportunities to materialize successfully there is an evident need to have an efficient communication and clear visualization of the new strategies and the underlying operational process among all the stakeholders involved in the manufacturing value chain. In this paper, we propose a model-based approach for creating and communicating business strategies (mission, goals, and tactics) and bridging the gap between the business strategies and corresponding operational processes using Business Motivation Model (BMM) and Business Process Modeling and Notation (BPMN) respectively. Concretely, we do the following, (i) create BMM models to communicate business ideas and connect corresponding business tactics to the processes models in BPMN, (ii) simulate processes to check achievable KPIs corresponding to different strategies (modeled in BMM), and (iii) define organizational units in BMM that perform tasks modeled in BPMN. We illustrate our approach through an exemplar case study about production of plastic cups by modeling high-level strategies and their underlying processes. To prove the feasibility of our work, we used and developed the BMM and BPMN plugins based on Eclipse Papyrus framework.

Robotic systems are a typical example of complex systems. Their design involves a combination of different technologies, requiring a multi-disciplinary approach. This is particularly challenging when a robotic system is required to interact either with humans or other entities within its environment. To tackle this complexity, we propose a design and validation approach based on MDE (Model-Driven Engineering) principles for industrial manipulators. We propose an extension of RobotML for manipulation, a modelling environment based on the Papyrus tool, which was developed specifically for the robotics domain. The extension is aiming to model a complete robotic setting, including protagonists, objects, their properties, the interactions between them, the services provided by the robots, and the actions they can perform. Then we propose to use model execution techniques to validate the design models. We illustrate our approach on a robotic scenario dedicated to the Sybot collaborative robot.

Robotic systems (RSs) are often used for performing critical tasks with little or no human intervention. Such RSs must satisfy certain dependability requirements including reliability, availability, security and safety. In this paper, we focus on the safety aspect and propose a methodology and associated framework for safety assessment of RSs in the early phases of development. The methodology relies upon model-driven engineering approach and describes a preliminary safety assessment of safety-critical RSs using fault tree (FT) analysis (FTA). The framework supports a domain specific language for RSs called RobotML and includes facilities (i) to automatically generate or manually construct FTs and perform both qualitative and quantitative FTA, (ii) to make semantic connections with formal verification and FTA tools, (iii) to represent FTA results in the RobotML modeling environment. In the case study, we illustrate the proposed methodology and framework by considering a mobile robot developed in the scope of the Proteus project.