Domain-Specific Language for Sensors in the Internet of Production

Bodenbenner, Matthias; Sanders, Mark P.; Montavon, Benjamin; Schmitt, Robert

doi:10.1007/978-3-662-62138-7_45

Cited by 16 publications

(9 citation statements)

References 15 publications

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“…For example, depending on the vendor, sensors might differ in their expressiveness or in the protocols that they support. Addressing this challenge on a system user's level, Bodenbenner et al [16] propose a domain-specific language that abstracts from the sensorspecific details and allows a unified access to the sensor information. However, their solution does not directly address how such sensors can actually be integrated and interconnected on a technical level.…”

Section: Interconnected and Industry-capable Infrastructurementioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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Section: Interconnected and Industry-capable Infrastructurementioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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“…Functions consist of a set of input arguments and a set of return parameters. Components are the structural representation of an asset, including an arbitrary number of children elements (Bodenbenner et al, 2020).…”

Section: Ontology-based Semantic and Syntactic Data Modelingmentioning

confidence: 99%

“…To define the components and the context Fig. 2 SOIL meta model (Bodenbenner et al, 2020) JSON document "header": { "style": "parameter", "description": "Date", "id": "PAR-LFD-123" }…”

Section: Definition Of Digital Twin and Modelmentioning

confidence: 99%

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Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems

et al. 2021

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The demand for individualized products drives modern manufacturing systems towards greater adaptability and flexibility. This increases the focus on data-driven digital twins enabling swift adaptations. Within the framework of cyber-physical systems, the digital twin is a digital model that is fully connected to the physical and digital assets. A digital model must follow a standardization for interoperable data exchange. Established ontologies and meta-models offer a basis in the definition of a schema, which is the first phase of creating a digital twin. The next phase is the standardized and structured modeling with static use-case specific data. The final phase is the deployment of digital twins into operation with a full connection of the digital model with the remaining cyber-physical system. In this deployment phase communication standards and protocols provide a standardized data exchange. A survey on the state-of-the-art of these three digital twin phases reveals the lack of a consistent workflow from ontology-driven definition to standardized modeling. Therefore, one goal of this paper is the design of an end-to-end digital twin pipeline to lower the threshold of creating and deploying digital twins. As the task of establishing a communication connection is highly repetitive, an automation concept by providing structured protocol data is the second goal. The planning and control of a line-less assembly system with manual stations and a mobile robot as resources and an industrial dog as the product serve as exemplary digital twin applications. Along this use-case the digital twin pipeline is transparently explained.

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“…While recent advances concerning CPS and the IIoT contribute to promising developments with an application in production [4], [10], [11], [14]- [16], [19], [21]- [23], [25], [31], [41], [45], [46], [52], [62], [65], [68], [70]- [72], [76], [77], [81], [83], [97]- [99], [108], [109], the need for security to enable novel forms of industrial collaboration must be especially considered for real-world deployments, where CPS are likely connected to the Internet. Apart from operational reasons (e.g., to ensure safety or privacy), secure approaches also convince traditionally conservative stakeholders, such as companies that fear for their competitiveness, to contribute to cross-domain and inter-company collaborations.…”

Section: A Towards An Internet Of Productionmentioning

confidence: 99%

Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

Pennekamp¹,

Buchholz²,

Dahlmanns³

et al. 2020

Proceedings 2020 Learning From Authoritative Security Experiment Results Workshop

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Following the recent Internet of Things-induced trends on digitization in general, industrial applications will further evolve as well. With a focus on the domains of manufacturing and production, the Internet of Production pursues the vision of a digitized, globally interconnected, yet secure environment by establishing a distributed knowledge base. Background. As part of our collaborative research of advancing the scope of industrial applications through cybersecurity and privacy, we identified a set of common challenges and pitfalls that surface in such applied interdisciplinary collaborations. Aim. Our goal with this paper is to support researchers in the emerging field of cybersecurity in industrial settings by formalizing our experiences as reference for other research efforts, in industry and academia alike. Method. Based on our experience, we derived a process cycle of performing such interdisciplinary research, from the initial idea to the eventual dissemination and paper writing. This presented methodology strives to successfully bootstrap further research and to encourage further work in this emerging area. Results. Apart from our newly proposed process cycle, we report on our experiences and conduct a case study applying this methodology, raising awareness for challenges in cybersecurity research for industrial applications. We further detail the interplay between our process cycle and the data lifecycle in applied research data management. Finally, we augment our discussion with an industrial as well as an academic view on this research area and highlight that both areas still have to overcome significant challenges to sustainably and securely advance industrial applications. Conclusions. With our proposed process cycle for interdisciplinary research in the intersection of cybersecurity and industrial application, we provide a foundation for further research. We look forward to promising research initiatives, projects, and directions that emerge based on our methodological work.

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Domain-Specific Language for Sensors in the Internet of Production

Cited by 16 publications

References 15 publications

A Computer Science Perspective on Digital Transformation in Production

A Computer Science Perspective on Digital Transformation in Production

Pipeline for ontology-based modeling and automated deployment of digital twins for planning and control of manufacturing systems

Collaboration is not Evil: A Systematic Look at Security Research for Industrial Use

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