Fault-Tolerant mHealth Framework in the Context of IoT-Based Real-Time Wearable Health Data Sensors

The emerging technology breakthrough of the Internet of Things (IoT) is expected to offer promising solutions for indoor/outdoor healthcare, which may contribute significantly to human health and well-being. In this paper, we investigated the technologies of healthcare service applications in telemedicine architecture. We aimed to resolve a series of healthcare problems on the frequent failures in telemedicine architecture through IoT solutions, particularly the failures of wearable body sensors (Tier 1) and a medical center server (Tier 3). For improved generalisability, we demonstrated an effective research approach, the fault-tolerant framework on mHealth or the so-called FTF-mHealth-IoT in the context of IoT, to resolve essential problems in current investigations on healthcare services. First, we propose a risk local triage algorithm known as the risk-level localization triage (RLLT), which can exclude the control process of patient triage from the medical center by using mHealth and can warn about failures related to wearable sensors. RLLT performs this initial step towards detecting a patient’s emergency case and then identifying the healthcare service package of the risk-level. Second, according to the risk-level package, our framework can aid decision makers in hospital selection through multi-criteria decision making (MCDM). Accordingly, mHealth can connect directly with the servers of distributed hospitals to ascertain available healthcare services for the risk-level package in those hospitals. The time of arrival of the patient at the hospital (TAH) is considered for each hospital to reach a final decision and select the appropriate institution in case of medical center failure. This paper used two datasets. The first dataset involved 572 patients with chronic heart disease. Their triage levels were evaluated using our RLLT algorithm. The second dataset included hospital healthcare services with two levels of availability within distributed hospitals to show variety when testing the proposed framework. The former dataset is an actual dataset of services collected from 12 hospitals located in the capital Baghdad, which represents the maximum level of availability. The latter is an assumption dataset of the services within the 12 hospitals located in the capital Kuala Lumpur, which represents the minimum level of availability. Subsequently, the hospitals were prioritized using a unique MCDM method for estimating small power consumption, namely, the analytic hierarchy process (AHP), based on a crossover between the “healthcare services package/TAH” of each hospital and the “hospital list”. The results showed that the AHP is effective for solving hospital selection problems within mHealth. The implications of this study support the patients, organizations, and medical staff in a modern lifestyle.