Abstract
The Internet of Things has the potential of transforming health systems through the collection and analysis of patient physiological data via wearable devices and sensor networks. Such systems can offer assisted living services in real-time and offer a range of multimedia-based health services. However, service downtime, particularly in the case of emergencies, can lead to adverse outcomes and in the worst case, death. In this paper, we propose an e-health monitoring architecture based on sensors that relies on cloud and fog infrastructures to handle and store patient data. Furthermore, we propose stochastic models to analyze availability and performance of such systems including models to understand how failures across the Cloud-to-Thing continuum impact on e-health system availability and to identify potential bottlenecks. To feed our models with real data, we design and build a prototype and execute performance experiments. Our results identify that the sensors and fog devices are the components that have the most significant impact on the availability of the e-health monitoring system, as a whole, in the scenarios analyzed. Our findings suggest that in order to identify the best architecture to host the e-health monitoring system, there is a trade-off between performance and delays that must be resolved.
Highlights
The rapid emergence, ubiquity, and convergence of social media, mobility, cloud computing, big data and data analytics, and the Internet of Things (IoT) are transforming how society operates and interacts with each other
We propose stochastic performance models integrated with the availability models, and real data outputted from the prototype in order to understand how different capacity of fog devices and different geo-location of cloud instances impact on performance metrics, such as throughput and service time
To understand the impact of e-health monitoring system unavailability resulting from fog and cloud failures, we model this scenario using a combination of Stochastic Petri Nets (SPN) and Reliability Block Diagrams (RBD)
Summary
The rapid emergence, ubiquity, and convergence of social media, mobility, cloud computing, big data and data analytics, and the Internet of Things (IoT) are transforming how society operates and interacts with each other. An analytical model is used in [15] to decide where to process the data obtained from the IoT devices considering renewable energy consumption and the Quality of Service (QoS) of the application To validate their model, the authors presented a video stream analysis application, where vehicles transmit data on road conditions, such as objects located on the road, to the cloud. The authors in [11] use a medical application as a case study for their proposed model analyzing the security of the information flow in IoT systems integrated with cloud infrastructures. Our work differs from these works on e-health applications because we consider an e-health I2C-based system that relies on edge, fog and cloud infrastructures Based on this novel and emerging scenario, we propose stochastic models to evaluate the availability and performance of this system in three different scenarios. We perform experiments using a prototype in order to obtain real data to use as an input to our stochastic models to give more realistic results and associated discussion
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