부산대학교 도서관

With the increased number of data and data-generating devices in healthcare settings, the health monitoring systems have started to experience issues, such as efficient processing and latency. Several health-monitoring systems have been designed using Wireless Sensors Networks (WSN), cloud computing, fog computing, and the Internet of Things (IoT). Most of the health monitoring systems have been designed using the cloud computing architecture. However, due to the high latency introduced by the cloud-based architecture while processing massive volumes of data, large-scale deployment of latency-sensitive healthcare applications is restricted. Fog computing that places computing servers closer to the users addresses the latency problems and increases the on-demand scaling, resource accessibility, and security dramatically. In this paper, we propose a fog-based health monitoring system architecture to minimize latency and network usage. We also present a new Load Balancing Scheme (LBS) to balance the load among fog nodes when the health monitoring system is deployed on a large scale. To validate the effectiveness of the proposed approach, we conducted extensive simulations in the iFogSim toolkit and compared the results with the cloud-only implementation, Fog Node Placement Algorithm (FNPA), and LoAd Balancing (LAB) scheme, in terms of latency and network usage. The proposed implementation of the health monitoring system significantly reduces latency and network usage compared to cloud-only, FNPA, and LAB Scheme.