Blockchain and ANFIS empowered IoMT application for privacy preserved contact tracing in COVID-19 pandemic

被引：37

作者：

Aslam B. ^{[1
]}

Javed A.R. ^{[2
]}

Chakraborty C. ^{[3
]}

Nebhen J. ^{[4
]}

Raqib S. ^{[1
]}

Rizwan M. ^{[1
]}

机构：

[1] Kinnaird College for Women University Lahore, Lahore

[2] Department of Cyber Security, Air University, Islamabad

[3] Department of Electronics, Communication Engineering, Birla Institute of Technology, Jharkhand

[4] College of Computer Science and Engineering, Prince Sattam bin Abdulaziz University, PO. Box: 151, Alkharj

来源：

Personal and Ubiquitous Computing | 2024年 / 28卷 / Suppl 1期

关键词：

Adaptive Neuro-Fuzzy Interference System (ANFIS); Anonymity; Blockchain; Contact tracking; COVID-19; Internet of Medical Things (IoMT); Mobile computing; Privacy; Security;

D O I：

10.1007/s00779-021-01596-3

中图分类号：

学科分类号：

摘要：

Life-threatening novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), also known as COVID-19, has engulfed the world and caused health and economic challenges. To control the spread of COVID-19, a mechanism is required to enforce physical distancing between people. This paper proposes a Blockchain-based framework that preserves patients’ anonymity while tracing their contacts with the help of Bluetooth-enabled smartphones. We use a smartphone application to interact with the proposed blockchain framework for contact tracing of the general public using Bluetooth and to store the obtained data over the cloud, which is accessible to health departments and government agencies to perform necessary and timely actions (e.g., like quarantine the infected people moving around). Thus, the proposed framework helps people perform their regular business and day-to-day activities with a controlled mechanism that keeps them safe from infected and exposed people. The smartphone application is capable enough to check their COVID status after analyzing the symptoms quickly and observes (based on given symptoms) either this person is infected or not. As a result, the proposed Adaptive Neuro-Fuzzy Interference System (ANFIS) system predicts the COVID status, and K-Nearest Neighbor (KNN) enhances the accuracy rate to 95.9% compared to state-of-the-art results. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

引用

页码：9 / 9

共 37 条

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