Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
121 tokens/sec
GPT-4o
9 tokens/sec
Gemini 2.5 Pro Pro
47 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Model-based Digital Twins of Medicine Dispensers for Healthcare IoT Applications (2312.04662v1)

Published 7 Dec 2023 in cs.SE

Abstract: Healthcare applications with the Internet of Things (IoT) are often safety-critical, thus, require extensive testing. Such applications are often connected to smart medical devices from various vendors. System-level testing of such applications requires test infrastructures physically integrating medical devices, which is time and monetary-wise expensive. Moreover, applications continuously evolve, e.g., introducing new devices and users and updating software. Nevertheless, a test infrastructure enabling testing with a few devices is insufficient for testing healthcare IoT systems, hence compromising their dependability. In this paper, we propose a model-based approach for the creation and operation of digital twins (DTs) of medicine dispensers as a replacement for physical devices to support the automated testing of IoT applications at scale. We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City and its industrial partners, providing healthcare services to its residents. We study the fidelity of DTs in terms of their functional similarities with their physical counterparts: medicine dispensers. Results show that the DTs behave more than 92% similar to the physical medicine dispensers, providing a faithful replacement for the dispenser.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (69)
  1. Andrea Arcuri. Restful api automated test case generation with evomaster. ACM Transactions on Software Engineering and Methodology (TOSEM), 28(1):1–37, 2019. doi: https://doi.org/10.1145/3293455.
  2. A practical guide for using statistical tests to assess randomized algorithms in software engineering. In Proceedings of the 33rd international conference on software engineering, pages 1–10, 2011. doi: https://doi.org/10.1145/1985793.1985795.
  3. A scalable framework for healthcare monitoring application using the internet of medical things. Software: Practice and Experience, 51(12):2457–2468, 2021. doi: https://doi.org/10.1002/spe.2849.
  4. Process-aware digital twin cockpit synthesis from event logs. Journal of Computer Languages, 70:101121, 2022. doi: https://doi.org/10.1016/j.cola.2022.101121.
  5. Digital twin as risk-free experimentation aid for techno-socio-economic systems. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems, pages 66–75, 2022. doi: https://doi.org/10.1145/3550355.3552409.
  6. Models@ run. time: a guided tour of the state of the art and research challenges. Software & Systems Modeling, 18:3049–3082, 2019. doi: https://doi.org/10.1007/s10270-018-00712-x.
  7. Compositional simulation of abstract state machines for safety critical systems. In Formal Aspects of Component Software: 18th International Conference, FACS 2022, Virtual Event, November 10–11, 2022, Proceedings, pages 3–19. Springer, 2022. doi: https://doi.org/10.1007/978-3-031-20872-0_1.
  8. Digital twin operational platform for connectivity and accessibility using flask python. In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 237–241. IEEE, 2021. doi: 10.1109/MODELS-C53483.2021.00042.
  9. Towards model-driven digital twin engineering: Current opportunities and future challenges. In Systems Modelling and Management: First International Conference, ICSMM 2020, Bergen, Norway, June 25–26, 2020, Proceedings 1, pages 43–54. Springer, 2020. doi: https://doi.org/10.1007/978-3-030-58167-1_4.
  10. Digital twins in health care: ethical implications of an emerging engineering paradigm. Frontiers in genetics, 9:31, 2018. doi: https://doi.org/10.3389/fgene.2018.00031.
  11. Antonio Bucchiarone. Gamification and virtual reality for digital twins learning and training: Architecture and challenges. Virtual Reality & Intelligent Hardware, 4(6):471–486, 2022. doi: https://doi.org/10.1016/j.vrih.2022.08.001.
  12. A novel methodology to construct digital twin models for spacecraft operations using fault and behaviour trees. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, pages 473–480, 2022. doi: https://doi.org/10.1145/3550356.3561550.
  13. Dtmn a modelling notation for digital twins. In Enterprise Design, Operations, and Computing. EDOC 2022 Workshops: IDAMS, SoEA4EE, TEAR, EDOC Forum, Demonstrations Track and Doctoral Consortium, Bozen-Bolzano, Italy, October 4–7, 2022, Revised Selected Papers, pages 63–78. Springer, 2023. doi: https://doi.org/10.1007/978-3-031-26886-1_4.
  14. Towards a model-driven architecture for interactive digital twin cockpits. In Conceptual Modeling: 39th International Conference, ER 2020, Vienna, Austria, November 3–6, 2020, Proceedings, pages 377–387. Springer, 2020. doi: https://doi.org/10.1007/978-3-030-62522-1_28.
  15. Generating customized low-code development platforms for digital twins. Journal of Computer Languages, 70:101117, 2022a. doi: https://doi.org/10.1016/j.cola.2022.101117.
  16. A cross-domain systematic mapping study on software engineering for digital twins. Journal of Systems and Software, page 111361, 2022b. doi: https://doi.org/10.1016/j.jss.2022.111361.
  17. Rescue: enabling green healthcare services using integrated iot-edge-fog-cloud computing environments. Software: Practice and Experience, 52(7):1615–1642, 2022. doi: https://doi.org/10.1002/spe.3078.
  18. Inference of simulation models in digital twins by reinforcement learning. In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 221–224. IEEE, 2021. doi: 10.1109/MODELS-C53483.2021.00038.
  19. Digital twins for anomaly detection in the industrial internet of things: Conceptual architecture and proof-of-concept. IEEE Transactions on Industrial Informatics, 2023. doi: 10.1109/TII.2023.3246983.
  20. Karie Medicine Dispenser. \urlhttps://kariehealth.com/, a. [Online; accessed 15-June-2023].
  21. Pilly SMS Medicine Dispenser. \urlhttps://responssenteret.no/responsskolen/brukere/manualer-videoer/Pilly.php, b. [Online; accessed 19-June-2023].
  22. Eclipse Ditto. \urlhttps://projects.eclipse.org/projects/iot.ditto. [Online; accessed 30-March-2023].
  23. Digital twin for intelligent context-aware iot healthcare systems. IEEE Internet of Things Journal, 8(23):16749–16757, 2021. doi: 10.1109/JIOT.2021.3051158.
  24. Flask. \urlhttps://flask.palletsprojects.com/en/2.2.x/. [Online; accessed 02-March-2023].
  25. Air quality management: An exemplar for model-driven digital twin engineering. In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 229–232. IEEE, 2021. doi: https://doi.org/10.1145/3365438.3410941.
  26. Hierarchical federated learning based anomaly detection using digital twins for smart healthcare. In 2021 IEEE 7th International Conference on Collaboration and Internet Computing (CIC), pages 16–25. IEEE, 2021. doi: 10.1109/CIC52973.2021.00013.
  27. Towards a heterogeneous iot privacy architecture. In Proceedings of the 35th Annual ACM Symposium on Applied Computing, pages 770–772, 2020. doi: https://doi.org/10.1145/3341105.3374108.
  28. Evolutionary mutation testing for iot with recorded and generated events. Software: Practice and Experience, 49(4):640–672, 2019. doi: https://doi.org/10.1002/spe.2629.
  29. Eclipse Hono. \urlhttps://www.eclipse.org/hono/. [Online; accessed 30-March-2023].
  30. Azure IoT Hub. \urlhttps://azure.microsoft.com/en-us/products/iot-hub/. [Online; accessed 30-March-2023].
  31. Standards-based interoperable digital twin in industry 4.0–a pilot demonstration. In 16th WCEAM Proceedings, pages 726–735. Springer, 2023. doi: https://doi.org/10.1007/978-3-031-25448-2_68.
  32. Model-driven digital twin construction: synthesizing the integration of cyber-physical systems with their information systems. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, pages 90–101, 2020. doi: https://doi.org/10.1145/3365438.3410941.
  33. Understanding and improving model-driven iot systems through accompanying digital twins. In Proceedings of the 20th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, pages 197–209, 2021. doi: https://doi.org/10.1145/3486609.3487210.
  34. Model-driven self-adaptive deployment of internet of things applications with automated modification proposals. ACM Transactions on Internet of Things, 3(4):1–30, 2022. doi: https://doi.org/10.1145/3549553.
  35. Revisiting visitors for modular extension of executable dsmls. In 2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS), pages 112–122. IEEE, 2017. doi: 10.1109/MODELS.2017.23.
  36. Aml4dt: a model-driven framework for developing and maintaining digital twins with automationml. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pages 1–8. IEEE, 2021. doi: 10.1109/ETFA45728.2021.9613376.
  37. Digital twin platforms: requirements, capabilities, and future prospects. IEEE Software, 39(02):53–61, 2022. doi: 10.1109/MS.2021.3133795.
  38. A domain-specific language for simulation-based testing of iot edge-to-cloud solutions. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems, pages 367–378, 2022. doi: https://doi.org/10.1145/3550355.3552405.
  39. Comparison between json and xml in applications based on ajax. In 2012 International Conference on Computer Science and Service System, pages 1174–1177. IEEE, 2012.
  40. EvoCLINICAL: Evolving Cyber-cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry System. In Proceedings of the 31th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2023. doi: 10.1145/3611643.3613897.
  41. Restest: automated black-box testing of restful web apis. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, pages 682–685, 2021. doi: https://doi.org/10.1145/3460319.3469082.
  42. Automatic Medicine Dispenser Medido. \urlhttps://medido.com/en/. [Online; accessed 19-June-2023].
  43. Towards development platforms for digital twins: A model-driven low-code approach. In Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems: IFIP WG 5.7 International Conference, APMS 2021, Nantes, France, September 5–9, 2021, Proceedings, Part I, pages 333–341. Springer, 2021. doi: https://doi.org/10.1007/978-3-030-85874-2_35.
  44. Generating digital twin cockpits for parameter management in the engineering of wind turbines. Modellierung 2022, 2022. doi: 10.18420/modellierung2022-012.
  45. Beyond discrete modeling: A continuous and efficient model for iot. In 2015 ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS), pages 90–99. IEEE, 2015. doi: 10.1109/MODELS.2015.7338239.
  46. Paula Muñoz. Measuring the fidelity of digital twin systems. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, pages 182–188, 2022. doi: https://doi.org/10.1145/3550356.3558516.
  47. Using uml and ocl models to realize high-level digital twins. In 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 212–220. IEEE, 2021. doi: 10.1109/MODELS-C53483.2021.00037.
  48. Using trace alignments for measuring the similarity between a physical and its digital twin. In Proceedings of the 25th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, pages 503–510, 2022. doi: https://doi.org/10.1145/3550356.3563135.
  49. Shyam Varan Nath and Pieter van Schalkwyk. Building industrial digital twins, 2021.
  50. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of molecular biology, 48(3):443–453, 1970.
  51. Digital twin for iot environments: A testing and simulation tool. In Quality of Information and Communications Technology: 15th International Conference, QUATIC 2022, Talavera de la Reina, Spain, September 12–14, 2022, Proceedings, pages 205–219. Springer, 2022. doi: https://doi.org/10.1007/978-3-031-14179-9_14.
  52. Eclipse Terms of Use. \urlhttps://www.eclipse.org/legal/termsofuse.php. [Online; accessed 30-March-2023].
  53. Modeling capabilities of digital twin platforms-old wine in new bottles. Journal of Object Technology, 21(3), 2022. ISSN 1660-1769. doi: http://dx.doi.org/10.5381/jot.2022.21.3.a10.
  54. PyEcore. \urlhttps://github.com/pyecore/pyecore. [Online; accessed 02-March-2023].
  55. Model-based simulation at runtime with abstract state machines. In Software Architecture: 14th European Conference, ECSA 2020 Tracks and Workshops, L’Aquila, Italy, September 14–18, 2020, Proceedings 14, pages 395–410. Springer, 2020. doi: https://doi.org/10.1007/978-3-030-59155-7_29.
  56. Hassan Sartaj. Automated approach for system-level testing of unmanned aerial systems. 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE), pages 1069–1073. IEEE, 2021. doi: 10.1109/ASE51524.2021.9678902.
  57. Hassan Sartaj. APD-DT: A Tool to Generate and Operate Digital Twins of Medicine Dispensers, April 2023. URL \urlhttps://github.com/Simula-COMPLEX/WTSPublic.
  58. Testing cockpit display systems of aircraft using a model-based approach. Software and Systems Modeling, pages 1–26, 2021. doi: 10.1007/s10270-020-00844-z.
  59. HITA: An architecture for system-level testing of healthcare iot applications. In Proceedings of the 17th European Conference on Software Architecture. Springer, 2023a. To Appear.
  60. Testing real-world healthcare iot application: Experiences and lessons learned. In Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2023, page 2044–2049. Association for Computing Machinery, 2023b. doi: 10.1145/3611643.3613888.
  61. Design, modeling and implementation of digital twins. Sensors, 22(14):5396, 2022. doi: https://doi.org/10.3390/s22145396.
  62. Behavior-centered digital-twin design for dynamic cyber-physical system development. Complex Systems Informatics and Modeling Quarterly (CSIMQ), 30:31–52, 2022. doi: 10.7250/csimq.2022-30.02.
  63. AWS IoT TwinMaker. \urlhttps://docs.aws.amazon.com/iot-twinmaker/. [Online; accessed 30-March-2023].
  64. Azure Digital Twins. \urlhttps://learn.microsoft.com/en-us/azure/digital-twins/. [Online; accessed 30-March-2023].
  65. Resttestgen: automated black-box testing of restful apis. In 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST), pages 142–152. IEEE, 2020. doi: 10.1109/ICST46399.2020.00024.
  66. Eclipse Vorto. \urlhttps://www.eclipse.org/vorto/. [Online; accessed 30-March-2023].
  67. Digital twin-based anomaly detection in cyber-physical systems. In 2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST), pages 205–216. IEEE, 2021. doi: 10.1109/ICST49551.2021.00031.
  68. Digital twin-based anomaly detection with curriculum learning in cyber-physical systems. ACM Transactions on Software Engineering and Methodology, 2023.
  69. Understanding digital twins for cyber-physical systems: a conceptual model. In Leveraging Applications of Formal Methods, Verification and Validation: Tools and Trends: 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, Rhodes, Greece, October 20–30, 2020, Proceedings, Part IV, pages 54–71. Springer, 2021. doi: https://doi.org/10.1007/978-3-030-83723-5_5.
Citations (7)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now