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

TL;DR

This paper introduces HITA, a scalable system architecture for testing healthcare IoT applications, featuring a digital twin generation component that balances fidelity and efficiency using model-based and machine learning methods.

Contribution

The paper presents a novel architecture for healthcare IoT system testing, including a digital twin generation approach evaluated for fidelity, scalability, and cost.

Findings

Model-based DTs achieve 94% fidelity.

ML-based DTs achieve 95% fidelity.

ML-based DTs have higher time costs but are scalable.

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.