A metaverse framework for IoT-based remote patient monitoring and virtual consultations using AES-256 encryption
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254957" target="_blank" >RIV/61989100:27240/24:10254957 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1568494624003624#sec0135" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494624003624#sec0135</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.asoc.2024.111588" target="_blank" >10.1016/j.asoc.2024.111588</a>
Alternative languages
Result language
angličtina
Original language name
A metaverse framework for IoT-based remote patient monitoring and virtual consultations using AES-256 encryption
Original language description
The convergence of Internet of Things (IoT) and metaverse technologies is revolutionizing healthcare. This study introduces a pioneering framework tailored for health monitoring within the metaverse. By reshaping remote patient monitoring and virtual consultations, the framework utilizes vital parameters like heart rate, blood pressure, and body temperature. It integrates IoT sensors, augmented reality (AR), and virtual reality (VR), establishing a cohesive metaverse environment for healthcare interactions. Notably, robust 256-bit AES encryption ensures data privacy and security. Our analysis highlights the pivotal role of metaverse architecture in healthcare, emphasizing the efficacy of AES-256 encryption in preserving patient confidentiality. Findings underscore the framework's potential to enhance remote patient care while upholding stringent data privacy standards. Moreover, it fosters trust among patients, healthcare providers, and regulatory bodies. In summary, this comprehensive framework marks a significant advancement in remote patient care, promising improved health outcomes and a secure foundation for healthcare in the metaverse.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Soft Computing
ISSN
1568-4946
e-ISSN
1872-9681
Volume of the periodical
158
Issue of the periodical within the volume
June 2024
Country of publishing house
US - UNITED STATES
Number of pages
24
Pages from-to
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UT code for WoS article
001229645800001
EID of the result in the Scopus database
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