Smart medical devices are an integral component of the healthcare Internet of Things (IoT), providing patients with various healthcare services through an IoT-based application. Ensuring the dependability of such applications through system and integration-level testing mandates the physical integration of numerous medical devices, which is costly and impractical. In this context, digital twins of medical devices play an essential role in facilitating testing automation. Testing with digital twins without accounting for uncertain environmental factors of medical devices leaves many functionalities of IoT-based healthcare applications untested. In addition, digital twins operating without environmental factors remain out of sync and uncalibrated with their corresponding devices functioning in the real environment. To deal with these challenges, in this paper, we propose a model-based approach (EnvDT) for modeling and simulating the environment of medical devices' digital twins under uncertainties. We empirically evaluate the EnvDT using three medicine dispensers, Karie, Medido, and Pilly connected to a real-world IoT-based healthcare application. Our evaluation targets analyzing the coverage of environment models and the diversity of uncertain scenarios generated for digital twins. Results show that EnvDT achieves approximately 61% coverage of environment models and generates diverse uncertain scenarios (with a near-maximum diversity value of 0.62) during multiple environmental simulations.

翻译：智能医疗设备是医疗物联网（IoT）不可或缺的组成部分，通过基于物联网的应用为患者提供多种医疗服务。通过系统和集成级测试确保此类应用的可靠性，需要将众多医疗设备进行物理集成，这既成本高昂又不切实际。在此背景下，医疗设备的数字孪生在促进测试自动化方面发挥着至关重要的作用。若使用数字孪生进行测试时未考虑医疗设备的不确定环境因素，则基于物联网的医疗应用中的诸多功能将无法得到测试。此外，未考虑环境因素的数字孪生与其在真实环境中运行的对应设备之间将无法保持同步且未经校准。为应对这些挑战，本文提出一种基于模型的方法（EnvDT），用于在不确定性条件下对医疗设备数字孪生的环境进行建模与仿真。我们使用连接到真实世界物联网医疗应用的三种药物分发器（Karie、Medido 和 Pilly）对 EnvDT 进行了实证评估。我们的评估旨在分析环境模型的覆盖率以及为数字孪生生成的不确定场景的多样性。结果表明，EnvDT 在多次环境仿真中实现了约 61% 的环境模型覆盖率，并生成了多样化的不确定场景（其多样性值接近最大值 0.62）。