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.

The vision of Industry 5.0 builds upon the advanced technologies of Industry 4.0, aligning its emerging concepts with three key pillars: human-centricity, sustainability, and resilience. In this context, the idea of the Product Digital Twin (PDT), first introduced in 2002, which has further matured (and refined) by the Asset Administration Shell (AAS) standard, must continue evolving to integrate the vision of Digital Product Passport (DPP) as promoted via several initiatives from the European Commission. Moreover, positioning PDT correctly within this broader context of Industry 5.0 is crucial, as it involves the modeling and implementation of PDT instances, which then significantly influence manufacturing applications. To address this need, this paper first harmonizes different PDT definitions and then proposes a modeling and deployment approach appropriate to the new concept of PDT information models.

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.

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

In the context of Industry 4.0, the analysis of Internet-of-Things (IoT) data with business intelligence (BI) technologies has acquired high relevance. However, designing and implementing IoT-based BI applications is hard for several reasons. Therefore, we propose a novel conceptual data model based on UML profiles and model-driven architecture (MDA) for modeling and implementing IoT-based BI applications. Our approach provides highly readable data models of IoT, which are also compatible with traditional BI data models. Furthermore, it could help in the implementation process of the IoT subsystem through automatic code generation.