A Modeling-Based Approach for Performance and Availability Assessment of IoMT Systems

The Internet of Things (IoT) enables remote monitoring of various environmental components through existing network infrastructures, thereby facilitating the integration of diverse computing systems. IoT systems encompass a wide range of devices and communication protocols, offering flexibility across various application domains. This adaptability makes IoT solutions particularly suitable for healthcare applications. For example, hospitals have implemented the Internet of Medical Things (IoMT) to collect and transmit patient data to healthcare professionals, as continuous monitoring is critical for patients in intensive care. Healthcare systems often demand high availability and have stringent performance requirements due to the necessity for rapid medical decision-making. However, the simultaneous assessment of performance and availability in IoMT systems is often overlooked. This paper introduces a modeling approach using stochastic Petri nets (SPNs) to evaluate both the availability and performance of IoMT systems. The approach also takes into account redundancy techniques, which may significantly improve system availability. The results highlight the practical feasibility of the proposed approach, demonstrating a reduction in downtime from 46.36 h to 0.21 h, while the response time remained constant. This indicates that the proposed modeling approach can enhance system availability without compromising performance. In addition, the proposed models adopt data collected from a real environment designed to support this approach. Furthermore, a sensitivity analysis was performed to identify the components that have a significant impact on system operation.