MontiThings: Model-Driven Development and Deployment of Reliable IoT Applications

Kirchhof, Jörg Christian; Rumpe, Bernhard; Schmalzing, David; Wortmann, Andreas

doi:10.1016/j.jss.2021.111087

Cited by 28 publications

(15 citation statements)

References 26 publications

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“…We expect that the need for heterogeneous models to describe different aspects of an application will grow. Some example domains are the modeling of structure and behavior of CPS used within digital twins (Bordeleau et al 2020;Brockhoff et al 2021), process models and resources defined in class diagrams for assistive systems which support human behavior , or the composition of class diagrams and architectural languages for the deployment of IoT systems (Kirchhof et al 2022). Using models from two languages where one has an inheritance relationship to the other one might be interesting for languages which are used in requirements engineering, where models can be underspecified, and then in system design, where implementation details have to be added.…”

Section: Discussionmentioning

confidence: 99%

Language Composition via Kind-Typed Symbol Tables.

Butting¹,

Michael²,

Rumpe³

2022

JOT

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The modularization of domain-specific modeling languages (DSMLs) fosters individual reuse of DSMLs in different contexts. Within this article, we discuss how it is possible to refer to model elements of other languages when composing different DSMLs. Related approaches usually rely on a DSML-agnostic language infrastructure that tests the compatibility of model elements via types encoded in Strings without any consistency checks. We propose the "strongly kind-typed" symbol table as an extension to the compiler approach to integrate the syntax of the languages using symbol tables that assign a symbol kind to each name definition. Our approach can be integrated into language workbenches that provide a symbol table infrastructure as part of a DSML implementation. The kind-typed symbol tables are integrated into the language workbench MontiCore. Strongly kind-typed symbol tables utilize the type system of the language workbench's host language to ensure type consistency between the language-specific symbol table infrastructures during DSML composition, which ultimately supports DSML engineering in the large.

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Section: Discussionmentioning

confidence: 99%

Language Composition via Kind-Typed Symbol Tables.

Butting¹,

Michael²,

Rumpe³

2022

JOT

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“…To increase productivity and reduce time to market, models are defined with concepts that are much less bound to their underlying implementation technology and much closer to the problem domain of interest [35]. However, due to the inherent complexity and heterogeneity present in the IoT domain, engineering platforms such as MDE4IoT [36], ThingML [37], IoTML/BRAIN-IoT [38,39], SimulateIoT [40] and Montithings [17] (to name a few), have demonstrated the potential to be realistic alternatives for developing scalable IoT systems leveraging MDE approaches. While that is the case, finding a platform capable of fully engineering such systems by integrating modeling, software development, system analysis, and deployment becomes challenging.…”

Section: Rp5: Supporting the Development And Deployment Of Iot System...mentioning

confidence: 99%

“…Furthermore, deploying IoT systems can be challenging, as they often operate in highly distributed and complex environments. Therefore, MDE tools must provide mechanisms for deploying and managing the system components in a scalable and efficient way [17]. This includes automatic deployment of software updates, efficient management of resources, and monitoring of the system's performance.…”

Section: Introductionmentioning

confidence: 99%

Low-Code Engineering for the Internet of Things

Ihirwe

2023

SSRN Journal

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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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MontiThings: Model-Driven Development and Deployment of Reliable IoT Applications

Cited by 28 publications

References 26 publications

Language Composition via Kind-Typed Symbol Tables.

Language Composition via Kind-Typed Symbol Tables.

Low-Code Engineering for the Internet of Things

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

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