Digital Twin (DT) is a core concept in the digitalization process in the Industry 4.0 and upcoming innovations such as Industry 5.0 and Metaverse. This digitalization can result in a huge amount of data that has not been collected before and now can help to understand and optimize processes and products. However, an important challenge is to really understand what the data can mean and then find out new information and knowledge that can be used for further optimizations. Therefore, it is important to have the virtual representations that the DT proposes as close as possible to the real-world application in a way that it can be understood in the same way by different people and systems. In this context, semantics annotations can be used to organize data by inserting knowledge into the model and providing a common understanding of a specific concept. This paper proposes a methodology for creating semantic digital twin models using the well-known tool called Node-RED. A sequence of steps is described that covers data acquisition, semantics annotations to this incoming data, assets modeling with their relationships which generates a graph of nodes and connections that is saved into a graph database. To be able to realize such a model, 7 so-called nodes have been developed for Node-RED. Finally, a use case has been implemented to demonstrate the application of the proposed methodology.

─Smart City is one area of application for the Internet of Things (IoT) and it has been attracting attention from both academia and industry. Cities will be composed by autonomous parts that communicate and provide services to each other. For instance, cars (autonomous or not) may be seen as a service that transports people from one point to another. Interactions between users and these kinds of services will grow, making it necessary to have digitization of all these parts of the Smart City. The Digital Twin (DT) concept proposes that real-world assets have a virtual representation connecting the physical world with the cyber world. This allows to track the whole life-cycle of this object as well as perform simulations with current or previously stored data. In this context, this work proposes the use of Digital Twin for enabling and supporting car-as-a-service (CaaS). A case study has been developed to demonstrate the modeling and the deployment of the Digital Twin, highlighting how this concept can be one of the key enablers for CaaS.

The digital twin (DT) is a virtual representation of a physical object, which has been proposed as one of the key concepts for Industry 4.0. The DT provides a virtual representation of products along their lifecycle that enables the prediction and optimization of the behavior of a production system and its components. A methodology design using model-driven engineering (MDE) is proposed that strives toward being both flexible and generic. This approach is presented at two levels: first, a DT is modeled as a composition of basic components that provide basic functionalities, such as identification, storage, communication, security, data management, human-machine interface (HMI), and simulation; second, an aggregated DT is defined as a hierarchical composition of other DTs. A generic reference architecture based on these concepts and a concrete implementation methodology are proposed using AutomationML. This methodology follows an MDE approach that supports most of the DT features currently proposed in the literature. A case study has been developed, the proposed ideas are being evaluated with industrial case studies, and some of the preliminary results are described in this article. With the case study, it is possible to verify that the proposed methodology supports the creation and the deployment process of a DT.

The Internet of Things concept is increasingly being used in projects from different areas. Often, projects encompass applications from different domains that need to share information for a common purpose. These types of systems can become complex because they involve different domains and it can be difficult to share information without misinterpreting certain information. Ontologies can be a way to describe information to be shared between different domains. The present work aims to propose a tool that facilitates the creation of Internet of Things systems using ontologies and through this model generate code automatically to pre-automate the integration of devices. A tool has been developed that facilitates the extraction of information from the ontology and it has been validated in the case study.

As the number of network connected devices in an industrial system increases, the management and handling of all the information generated become a challenge. In this work we explore concepts of Cyber-Physical System model the virtual part of industrial devices (sensor, machines, CLPs) using the Digital Twin concept and propose an architecture based on web services for accessing their data. We present a case study where an Augmented Reality system access the Twin Model data via web services and display real-time information to the user. Moreover, we present a review of how the involved concepts, which have a strong computational background, relate to industrial applications and how they can expand the possibility of services and business models.