Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT  

作　　者：G.Ravikumar K.Venkatachalam Mohammed A.AlZain Mehedi Masud Mohamed Abouhawwash 

机构地区：[1]Department of Computer Science and Engineering,Coimbatore Institute of Engineering and Technology,Coimbatore,641109,India [2]Department of Applied Cybernetics,Faculty of Science,University of Hradec Králové,50003,Hradec Králové,Czech Republic [3]Department of Information Technology,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif,21944,Saudi Arabia [4]Department of Computer Science,College of Computers and Information Technology,Taif University,P.O.Box 11099,Taif,21944,Saudi Arabia [5]Department of Mathematics,Faculty of Science,Mansoura University,Mansoura,35516,Egypt [6]Department of Computational Mathematics,Science,and Engineering(CMSE),Michigan State University,East Lansing,MI,48824,USA

出　　处：《Computer Systems Science & Engineering》2023年第1期945-959,共15页计算机系统科学与工程（英文）

基　　金：Taif University Researchers Supporting Project Number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.

摘　　要：Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

关 键 词：Sleep apnea POLYSOMNOGRAPHY IOMT fog node security neural network KNN signature encryption sensor 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]