Abstract
System-level testing of healthcare Internet of Things (IoT) applications requires creating a test infrastructure with integrated medical devices and third-party applications. A significant challenge in creating such test infrastructure is that healthcare IoT applications evolve continuously with the addition of new medical devices from different vendors and new services offered by different third-party organizations following different architectures. Moreover, creating test infrastructure with a large number of different types of medical devices is time-consuming, financially expensive, and practically infeasible. Oslo City’s healthcare department faced these challenges while working with various healthcare IoT applications. To address these challenges, this paper presents a real-world test infrastructure software architecture (HITA) designed for healthcare IoT applications. We evaluated HITA’s digital twin (DT) generation component implemented using model-based and machine learning (ML) approaches in terms of DT fidelity, scalability, and time cost of generating DTs. Results show that the fidelity of DTs created using model-based and ML approaches reach 94% and 95%, respectively. Results from operating 100 DTs concurrently show that the DT generation component is scalable and ML-based DTs have a higher time cost.
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Acknowledgements
This work is a part of the WTT4Oslo project (No. 309175) funded by the Research Council of Norway. All the experiments reported in this paper are conducted in a laboratory setting of Simula Research Laboratory; therefore, they do not by any means reflect the quality of services Oslo City provides to its citizens. Finally, we would like to acknowledge Kjetil Moberg for providing feedback on the initial version of this paper.
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Sartaj, H., Ali, S., Yue, T., Gjøby, J.M. (2024). HITA: An Architecture for System-level Testing of Healthcare IoT Applications. In: Tekinerdoğan, B., Spalazzese, R., Sözer, H., Bonfanti, S., Weyns, D. (eds) Software Architecture. ECSA 2023 Tracks, Workshops, and Doctoral Symposium. ECSA 2023. Lecture Notes in Computer Science, vol 14590. Springer, Cham. https://doi.org/10.1007/978-3-031-66326-0_28
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