Fog Data Processing and Analytics for Health Care-Based IoT Applications

Abstract

Healthcare solutions have been improved with the onset of data processing and data analytics in the connected ecosystem. Healthcare 4.0 is all about providing personalized health care with the amalgamation of Internet of Things (IoT) devices. These devices collect and transfer the health data which is stored and processed in cloud. Cloud computing delivers accurate insights from that data but providing care in emergency demands immediate access to huge volume of data and performing analytics on it. This hampers the speed of decision-making ultimately leading to increased latency, cumbersome data storage, soaring operational costs which can’t be appropriate for chronic diseases such as Parkinson’s, Vertigo and other neurological diseases as those require continuous monitoring and real-time response. Introducing the fog layer between IoT devices and Cloud Computing helps in reducing data transmission overheads and minimizes the burden on cloud. It has the potential to tackle the issues of cloud computing by providing local data storage, suburbanized data analytics resulting into increased productivity. Data processing on fog computing should be done while considering the difference in data models, naming conventions and processing paradigms which includes stream processing, batch processing and serverless functions. Deploying fog node as analytic engine in healthcare applications can reduce the data flow on the network and provides round-the-clock engagement with patient, improved preventive care. The use cases of fog deployment can be effective remote monitoring system, equipment monitoring, smart equipment maintenance.