Open Access

ARTICLE

An IoT Based Secure Patient Health Monitoring System

by Kusum Yadav¹, Ali Alharbi¹, Anurag Jain^2,*, Rabie A. Ramadan¹

1 College of Computer Science and Engineering, University of Ha’il, Ha’il, Kingdom of Saudi Arabia
2 Virtualization Department, School of Computer Science, University of Petroleum and Energy Studies, Dehradun-248007, Uttarakhand, India

* Corresponding Author: Anurag Jain. Email:

(This article belongs to the Special Issue: Integrity and Multimedia Data Management in Healthcare Applications using IoT)

Computers, Materials & Continua 2022, 70(2), 3637-3652. https://doi.org/10.32604/cmc.2022.020614

Received 31 May 2021; Accepted 11 July 2021; Issue published 27 September 2021

Abstract

Internet of things (IoT) field has emerged due to the rapid growth of artificial intelligence and communication technologies. The 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 management. However, the usage of IoT in the healthcare domain will become a nightmare if patient information is not securely maintained while transferring over an insecure network or storing at the administrator end. In this manuscript, the authors have developed a secure IoT healthcare monitoring system using the Blockchain-based XOR Elliptic Curve Cryptography (BC-XORECC) technique to avoid various vulnerable attacks. Initially, the work has established an authentication process for patient details by generating tokens, keys, and tags using Length Ceaser Cipher-based Pearson Hashing Algorithm (LCC-PHA), Elliptic Curve Cryptography (ECC), and Fishers Yates Shuffled Based Adelson-Velskii and Landis (FYS-AVL) tree. The authentications prevent unauthorized users from accessing or misuse the data. After that, a secure data transfer is performed using BC-XORECC, which acts faster by maintaining high data privacy and blocking the path for the attackers. Finally, the Linear Spline Kernel-Based Recurrent Neural Network (LSK-RNN) classification monitors the patient’s health status. The whole developed framework brings out a secure data transfer without data loss or data breaches and remains efficient for health care monitoring via IoT. Experimental 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 compared with the existing state-of-the-art method.

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Keywords

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