Healthcare Model Implementation and Effectiveness Analysis

Abstract

With ever-changing needs, interests, and preferences of healthcare system stakeholders, effective scaling of data processing is yet to be satisfactory, especially regarding the need to ensure that centralized cloud settings’ requirements are fulfilled. Indeed, this problem has arisen from the existence of deadline oriented cloud attributes that include command control systems, flight control systems, and health monitoring, platforms that call for minimum response time and latency. Also, the problem arises from a situation in which large data amounts continue to be transmitted, translating into a big data component. Therefore, performance degradation tends to arise when there is an interaction between IoT applications and centralized databases that host the big data. To address latency or delay, one of the innovative solutions involves fog computing, which also responds to issues of network congestion and resource contention. Through fog computing, there is the tension of clouds to network edges. In this study, a fog-enabled information framework was proposed. The proposed framework was that which strived to offer healthcare in the form of a cloud service, with the process achieved via the utilization of IoT devices. From the results, the framework was able to manage heart patients’ data efficiently. It is also notable that the proposed model’s performance was evaluated by using iFogSim toolkit.