An IoT Based Secure Patient Health Monitoring System

作     者：Kusum Yadav Ali Alharbi Anurag Jain Rabie A.Ramadan 

作者机构：College of Computer Science and EngineeringUniversity of Ha’ilHa’ilKingdom of Saudi Arabia Virtualization DepartmentSchool of Computer ScienceUniversity of Petroleum and Energy StudiesDehradun-248007UttarakhandIndia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第2期

页      面：3637-3652页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This project has been funded by the Scientific Research Deanship at the University of Ha’il-Saudi Arabia through project number BA-2105 

主　　题：Internet of things blockchain-based XOR elliptic curve cryptography linear spline kernel-based recurrent neural network health care monitoring length Ceaser cipher-based Pearson hashing algorithm elliptic curve cryptography fishers yates shuffled based Adelson-Velskii and Landis tree 

摘      要：Internet of things(IoT)field has emerged due to the rapid growth of artificial intelligence and communication *** use of IoT technology in modern healthcare environments is convenient for doctors and patients as it can be used in real-time monitoring of patients,proper administration of patient information,and healthcare ***,the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintainedwhile transferring over an insecure network or storing at the administrator *** this manuscript,the authors have developed a secure IoT healthcare monitoring system using the Blockchainbased XOR Elliptic Curve Cryptography(BC-XORECC)technique to avoid various vulnerable ***,thework has established an authentication process for patient details by generating tokens,keys,and tags using Length Ceaser Cipher-based PearsonHashingAlgorithm(LCC-PHA),EllipticCurve Cryptography(ECC),and Fishers Yates Shuffled Based Adelson-Velskii and Landis(FYS-AVL)*** authentications prevent unauthorized users from accessing or misuse the *** that,a secure data transfer is performed using BC-XORECC,which acts faster by maintaining high data privacy and blocking the path for the ***,the Linear Spline Kernel-Based Recurrent Neural Network(LSK-RNN)classification monitors the patient’s health *** whole developed framework brings out a secure data transfer without data loss or data breaches and remains efficient for health care monitoring via *** analysis shows that the proposed framework achieves a faster encryption and decryption time,classifies the patient’s health status with an accuracy of 89%,and remains robust comparedwith the existing state-of-the-art method.