A Comprehensive Overview of Fog Data Processing and Analytics for Healthcare 4.0

Abstract

AbstractIn recent technological era, the healthcare industry has been gaining momentum toward service-oriented facilities to the customers. The primary aim of the Healthcare 4.0 is to provide healthcare services anytime and anywhere. This is possible, as Healthcare 4.0 targets integration with current technologies like Internet of Things (IoT), Cloud Computing, Big Data, and Machine Learning. However, the integration of Healthcare 4.0, with IoT and cloud computing through Internet has several challenges in handling real-time applications such as access latency, cost, and lack of service availability. On the other hand, the fog computing (FC) is able to overcome these challenges using fog devices, that are capable of optimizing the delay in information gathering and processing. The primary advantage of the fog computing system is the geographical location of fog devices within proximity of patient and IoT healthcare systems. This enables fog computing system to perform computation, storage and networking services with lesser delay and jitter. However, the major issue in fog computing is to handle the voluminous healthcare data generate from different IoT healthcare edge systems. Hence, this chapter targets to present on various fog-based data processing and data analysis (FDPA) mechanisms in fog computing solutions toward achieving the objectives of Healthcare 4.0. This chapter is divided into five major sections namely architecture of fog data processing and analytics, applications of FDPA, data processing algorithms in fog computing and data compression mechanisms and data analysis mechanisms in Fog computing toward Healthcare 4.0. The fog data architecture discusses various layers namely sensing layer, fog gateway layer, fog-based data processing and data analysis layer, cloud layer, and service layer. Here, the process of sensing of healthcare data, maintenance of data, and various methods to analyze healthcare data are discussed. Further, the healthcare data gathered from sensor devices are raw and redundant in data, hence they are needed for various data processing algorithms for fog-based healthcare systems. Hence, various data processing techniques such as Dynamic Time Warping, Clinical Speech Processing are discussed. Next, in fog computing, the data compression techniques are required for optimizing the bandwidth consumption and energy efficiency are presented. Next, data analytics mechanism such as real-time decisive analysis, real-time control and context analysis, and real-time data analysis are presented.KeywordsInternet of ThingsFog computingHealthcareData processingData analyticsBig Data