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HITA: An Architecture for System-level Testing of Healthcare IoT Applications

Hassan Sartaj, Shaukat Ali, Tao Yue, Julie Marie Gjøby

TL;DR

HITA introduces a real-world test infrastructure for healthcare IoT applications, addressing the evolution of medical devices and third-party services by deploying digital twins (DTGen) and test stubs (TSGen) to enable scalable, automated system-level testing. The architecture supports secure, interoperable testing via REST/JSON, with DTs replacing physical devices to reduce risk and cost. Empirical evaluation on a Medido-based setup shows DT fidelity of $94\%$ (model-based) and $95\%$ (ML-based), and scalable operation of $100$ DTs, while ML-based DTs incur higher generation time. The work provides practical lessons on the trade-offs between DT approaches, data requirements, domain-specific testing, and test optimization, with implications for broader IoT domains beyond healthcare. The findings demonstrate HITA's potential to streamline regulatory-compliant, rapid-release testing in complex, heterogeneous IoT ecosystems and guide future extensions to DTGen/TSGen and domain-focused testing strategies.

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.

HITA: An Architecture for System-level Testing of Healthcare IoT Applications

TL;DR

HITA introduces a real-world test infrastructure for healthcare IoT applications, addressing the evolution of medical devices and third-party services by deploying digital twins (DTGen) and test stubs (TSGen) to enable scalable, automated system-level testing. The architecture supports secure, interoperable testing via REST/JSON, with DTs replacing physical devices to reduce risk and cost. Empirical evaluation on a Medido-based setup shows DT fidelity of (model-based) and (ML-based), and scalable operation of DTs, while ML-based DTs incur higher generation time. The work provides practical lessons on the trade-offs between DT approaches, data requirements, domain-specific testing, and test optimization, with implications for broader IoT domains beyond healthcare. The findings demonstrate HITA's potential to streamline regulatory-compliant, rapid-release testing in complex, heterogeneous IoT ecosystems and guide future extensions to DTGen/TSGen and domain-focused testing strategies.

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.
Paper Structure (43 sections, 3 figures, 3 tables)

This paper contains 43 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. HITA: An Architecture for Test Infrastructure
  4. HITA Components
  5. Healthcare IoT Core.
  6. Medical Devices - DTGen Component.
  7. Third-party Applications - TSGen Component.
  8. Testing Process.
  9. HITA Operational Context.
  10. Quality Attributes
  11. Scalability.
  12. Maintainability.
  13. Extensibility.
  14. Evolvability.
  15. Heterogeneity.
  16. ...and 28 more sections

Figures (3)

  • Figure 1: An overview of HITA. The arrow () shows one-way information flow, () indicates two-way information flow, () exhibits behavior simulation, and () depicts optional information flow.
  • Figure 2: Evaluation setup with a healthcare IoT application, testing tool, DTGen component, and physical device (PD).
  • Figure 3: RQ2: Scalability of multiple MBDTs and MLDTs running in different batch sizes