Fog Networks in Healthcare Application

Fog computing is a recently proposed computing paradigm that extends Cloud computing and services to the edge of the network. The new features offered by fog computing (e.g., distributed analytics and edge intelligence), if successfully applied for time-sensitive healthcare applications, has great potential to accelerate the discovery of early notification of emergency situations to support smart decision making. While promising, how to design and develop real-world fog computing-based data monitoring system is still an open question. As a first step to answer this question, in this research, we employ a fog-based cloud paradigm for time-sensitive medical applications and also propose to show the practical applicability and significance of such a novel system. The ubiquitous deployment of mobile and sensor devices is creating a new environment, namely the Internet of Things (IoT) that enables a wide range of future Internet applications. In this work, we present dynamic Fog, a high level programming model for time-sensitive applications that are geospatially distributed, large–scale, and latency–sensitive. We also analyze our fog model with healthcare data, more specifically with Heartrate data that is one of the most time-sensitive medical data which deals with life and death situations. Our experiments show that our proposed system achieves minimum delay while it also achieves the data accuracy and data consistency which are very important in many applications like medical data.