Multi-route plan for reliable services in fog computing-based health care monitoring systems

Abstract

Fog computing is a network architecture that extends the traditional centralized cloud computing capabilities to the edge of the network and closer to where the data is generated in which fog nodes provide data processing and storage services to the end devices located within its zone. The main concern of fog computing paradigm is to reduce data transmission to the cloud which saves the bandwidth and mitigates the burden of processing on the cloud. Moreover, due to the shorter distance between the end-user and the fog nodes, users can get faster delivery of the services compared to the cloud. Therefore, fog computing is more suitable to handle time-sensitive situations like the critical data provided by the Internet of Things (IoT) technology. This may include sensory healthcare data, disaster detection data, and critical business data which needs rapid processing to make real-time decisions to prevent the harm or mitigate its effect. However, it is necessary to ensure the services’ availability of these systems especially with the existence of fog node failure. The issue of service interruption during fog node failure in time-sensitive applications has not received much attention. Particularly for fog-based healthcare monitoring systems in which the rapid response for certain situations is needed such as detecting high blood pressure, heartbeat rate, and sugar level. Thus, providing efficient and reliable fog-based healthcare monitoring systems is necessary to ensure sustainable time-sensitive services as it is related to human life. This research work has reviewed and examined many relevant healthcare monitoring systems that exploited fog computing in their implementation. The focus is given on investigating the impact of fog node failure on healthcare monitoring systems’ services. Furthermore, this research has employed the features of fog computing to propose a multi-route plan that aims to identify an alternative route to ensure the reliability of the services and avoid the impact of any interruption during critical situations. The proposed approach attempts to allocate the next nearest fog node as an alternative route if the primary fog node fails to accomplish the given task. Various real-life scenarios have been designed and implemented demonstrating different cases to evaluate the performance of the proposed strategy. The experimental results of this study illustrate that incorporating a fog-based multi-route plan in the implementation of time-sensitive systems such as healthcare monitoring systems would achieve less latency, lower energy consumption levels, and reasonable network use and execution cost that maintains high-reliability requirements compared to the most recent related work.