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A taxonomy on smart healthcare technologies : security framework, case study, and future directions

dc.contributor.authorChaudhary, Sachien_US
dc.contributor.authorKakkar, Riyaen_US
dc.contributor.authorJadav, Nilesh Kumaren_US
dc.contributor.authorNair, Anujaen_US
dc.contributor.authorGupta, Rajeshen_US
dc.contributor.authorTanwar, Sudeepen_US
dc.contributor.authorAgrawal, Smitaen_US
dc.contributor.authorAlshehri, Mohammad Dahmanen_US
dc.contributor.authorSharma, Ravien_US
dc.contributor.authorSharma, Gulshanen_US
dc.contributor.authorDavidson, Innocent E.en_US
dc.date.accessioned2022-08-02T09:36:18Z
dc.date.available2022-08-02T09:36:18Z
dc.date.issued2022-07-05
dc.date.updated2022-07-09T21:35:01Z
dc.description.abstractThere 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 technologiesen_US
dc.format.extent30 p.en_US
dc.identifier.citationChaudhary, S. et al. 2022. A taxonomy on smart healthcare technologies: security framework, case study, and future directions. Journal of Sensors. 2022: 1-30. doi:10.1155/2022/1863838en_US
dc.identifier.doi10.1155/2022/1863838
dc.identifier.issn1687-725X
dc.identifier.issn1687-7268 (Online)
dc.identifier.urihttps://hdl.handle.net/10321/4181
dc.language.isoenen_US
dc.publisherHindawi Limiteden_US
dc.relation.ispartofJournal of Sensors; Vol. 2022en_US
dc.subject0303 Macromolecular and Materials Chemistryen_US
dc.subject0306 Physical Chemistry (incl. Structural)en_US
dc.titleA taxonomy on smart healthcare technologies : security framework, case study, and future directionsen_US
dc.typeArticleen_US
local.sdgSDG14

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