Please use this identifier to cite or link to this item:
https://hdl.handle.net/10321/4181
Title: | A taxonomy on smart healthcare technologies : security framework, case study, and future directions | Authors: | Chaudhary, Sachi Kakkar, Riya Jadav, Nilesh Kumar Nair, Anuja Gupta, Rajesh Tanwar, Sudeep Agrawal, Smita Alshehri, Mohammad Dahman Sharma, Ravi Sharma, Gulshan Davidson, Innocent E. |
Keywords: | 0303 Macromolecular and Materials Chemistry;0306 Physical Chemistry (incl. Structural) | Issue Date: | 5-Jul-2022 | Publisher: | Hindawi Limited | Source: | Chaudhary, S. et al. 2022. A taxonomy on smart healthcare technologies: security framework, case study, and future directions. Journal of Sensors. 2022: 1-30. doi:10.1155/2022/1863838 | Journal: | Journal of Sensors; Vol. 2022 | Abstract: | There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies |
URI: | https://hdl.handle.net/10321/4181 | ISSN: | 1687-725X 1687-7268 (Online) |
DOI: | 10.1155/2022/1863838 |
Appears in Collections: | Research Publications (Engineering and Built Environment) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Journal of Sensors Copyright Clearance.docx | Copyright Clearance | 190.18 kB | Microsoft Word XML | View/Open |
Davidson_IE et al_2022.pdf | Article | 1.27 MB | Adobe PDF | View/Open |
Page view(s)
260
checked on Dec 16, 2024
Download(s)
55
checked on Dec 16, 2024
Google ScholarTM
Check
Altmetric
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.