Abstract The increasing complexity delivered by the heterogeneity of the cyber-physical systems is being addressed and decoded by edge technologies, IoT development, robotics, digital twin engineering, and AI. Nevertheless, tackling the orchestration of these complex ecosystems has become a challenging problem. Specially the inherent entanglement of the different emerging technologies makes it hard to maintain and scale such ecosystems. In this context, the usage of model-driven engineering as a more abstract form of glue-code, replacing the boilerplate fashion, has improved the software development lifecycle, democratising the access to and use of the aforementioned technologies. In this paper, we present a practical use case in the context of Smart Manufacturing, where we use several platforms as providers of a high-level abstraction layer, as well as security measures, allowing a more efficient system construction and interoperability.

Abstract In the Internet of Things (IoT) era, devices and systems generate enormous amounts of real-time data, and demand real-time analytics in an uninterrupted manner. The typical solution, a cloud-centred architecture providing an analytics service, cannot guarantee real-time responsiveness because of unpredictable workloads and network congestion. Recently, edge computing has been proposed as a solution to reduce latency in critical systems. For computation processing and analytics on edge, the challenges include handling the heterogeneity of devices and data, and achieving processing on the edge in order to reduce the amount of data transmitted over the network. In this paper, we show how low-code, model-driven approaches benefit a Digital Platform for Edge analytics. The first solution uses EdgeX, an IIoT framework for supporting heterogeneous architectures with the eKuiper rule-based engine. The engine schedules fully automatically tasks that retrieve data from the Edge, as the infrastructure near the data is generated, allowing us to create a continuous flow of information. The second solution uses FiWARE, an IIoT framework used in industry, using IoT agents to accomplish a pipeline for edge analytics. In our architecture, based on the DIME LC/NC Integrated Modelling Environment, both integrations of EdgeX/eKuyper and FiWARE happen by adding an External Native DSL to this Digital Platform. The DSL comprises a family of reusable Service-Independent Building blocks (SIBs), which are the essential modelling entities and (service) execution capabilities in the architecture’s modelling layer. They provide users with capabilities to connect, control and organise devices and components, and develop custom workflows in a simple drag and drop manner.

Abstract With the heterogeneity of the industry 4.0 world, and more generally of the Cyberphysical Systems realm, the quest towards a platform approach to solve the interoperability problem is front and centre to any system and system-of-systems project. Traditional approaches cover individual aspects, like data exchange formats and published interfaces. They may adhere to some standard, however they hardly cover the production of the integration layer, which is implemented as bespoke glue code that is hard to produce and even harder to maintain. Therefore, the traditional integration approach often leads to poor code quality, further increasing the time and cost and reducing the agility, and a high reliance on the individual development skills. We are instead tackling the interoperability challenge by building a model driven/low-code Digital Thread platform that 1) systematizes the integration methodology, 2) provides methods and techniques for the individual integrations based on a layered Domain Specific Languages (DSL) approach, 3) through the DSLs it covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable, 4) showcases a first collection of examples from the domains of robotics, IoT, data analytics, AI/ML and web applications, 5) brings cohesiveness to the aforementioned heterogeneous platform, and 6) is easier to understand and maintain, even by not specialized programmers. We showcase the power, versatility and the potential of the Digital Thread platform on four interoperability case studies: the generic extension to REST services, to robotics through the UR family of robots, to the integration of various external databases (for data integration) and to the provision of data analytics capabilities in R.