%0 Journal Article
%@ 19326203
%A Shukla, S.
%A Hassan, M.F.
%A Khan, M.K.
%A Jung, L.T.
%A Awang, A.
%D 2019
%F scholars:11162
%I Public Library of Science
%J PLoS ONE
%K algorithm; analytic method; Article; artificial neural network; data collection method; electronic medical record; fog computing; fuzzy system; health care system; human; information technology; internet of things; latent period; reinforcement learning (machine learning); sampling; cloud computing; computer interface; computer network; computer simulation; data base; electrocardiography; fuzzy logic; health care delivery; support vector machine; theoretical model, Cloud Computing; Computer Communication Networks; Computer Simulation; Databases as Topic; Delivery of Health Care; Electrocardiography; Fuzzy Logic; Internet of Things; Models, Theoretical; Support Vector Machine; User-Computer Interface
%N 11
%R 10.1371/journal.pone.0224934
%T An analytical model to minimize the latency in healthcare internet-of-things in fog computing environment
%U https://khub.utp.edu.my/scholars/11162/
%V 14
%X Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT�FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods. © 2019 Shukla et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
%Z cited By 62