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CPSAML: a language and code generation framework for digital twin based monitoring of mobile cyber-physical systems

Published:09 November 2022Publication History

ABSTRACT

Cyber-physical systems (CPS) are finding increasing use, whether in factories, autonomous vehicles, or smart buildings. Monitoring the execution of CPSs is crucial since CPSs directly influence their physical environment. Like the actual system, the monitoring application must be designed, developed, and tested. Mobile CPSs, in contrast to stationary CPSs, bring the additional requirement that instances can dynamically join, leave, or fail during execution time. This dynamic behavior must also be considered in the monitoring application. This paper presents CPSAML, a language and code generation framework for the model-driven development of mobile CPS systems, including a cockpit application for monitoring and interacting with such a system. The pipeline starts with the formulation of the system and the CPSs it contains at an abstract level by the system architect using a domain-specific modeling language. Next, this model is transformed into SysML 2 for further extension and richer specificity by system engineers on a more technical level. In the last step of the pipeline, the SysML 2 model is used to generate code for the CPS devices, a system-wide digital twin, and the cockpit application mentioned above. This cockpit enables the operator to configure and apply the monitoring and interaction with the system during runtime. We evaluate our CPSAML language and code generation framework on an Indoor Transport System case study with Roomba vacuum cleaner robots.

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      • Published in

        cover image ACM Conferences
        MODELS '22: Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
        October 2022
        1003 pages
        ISBN:9781450394673
        DOI:10.1145/3550356
        • Conference Chairs:
        • Thomas Kühn,
        • Vasco Sousa

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        This work is licensed under a Creative Commons Attribution International 4.0 License.

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        Association for Computing Machinery

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        • Published: 9 November 2022

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