ASC Performance Prediction for Medical IoT Communication Networks
Wearable devices are gradually entering the medical health field. Medical Internet of Things (IoT) has been widely used in all walks of medical health. With the complexity of medical health application scenarios, the medical IoT communication networks face complex environments. The secure communicat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178003/ https://www.ncbi.nlm.nih.gov/pubmed/34136110 http://dx.doi.org/10.1155/2021/6265520 |
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author | Yin, Fagen Xiao, Pingping Li, Zefeng |
author_facet | Yin, Fagen Xiao, Pingping Li, Zefeng |
author_sort | Yin, Fagen |
collection | PubMed |
description | Wearable devices are gradually entering the medical health field. Medical Internet of Things (IoT) has been widely used in all walks of medical health. With the complexity of medical health application scenarios, the medical IoT communication networks face complex environments. The secure communication issue is very important for medical IoT communication networks. This paper investigates the secrecy performance of medical IoT communication networks. To improve the secrecy performance, we adopt a cooperative communication strategy. We also use the average secrecy capacity (ASC) as a metric, and the expressions are first derived. Then, a secrecy performance intelligent prediction algorithm is proposed. The extensive simulations are used to verify the proposed method. Compared with other methods, the proposed algorithm realizes a better prediction precision. |
format | Online Article Text |
id | pubmed-8178003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-81780032021-06-15 ASC Performance Prediction for Medical IoT Communication Networks Yin, Fagen Xiao, Pingping Li, Zefeng J Healthc Eng Research Article Wearable devices are gradually entering the medical health field. Medical Internet of Things (IoT) has been widely used in all walks of medical health. With the complexity of medical health application scenarios, the medical IoT communication networks face complex environments. The secure communication issue is very important for medical IoT communication networks. This paper investigates the secrecy performance of medical IoT communication networks. To improve the secrecy performance, we adopt a cooperative communication strategy. We also use the average secrecy capacity (ASC) as a metric, and the expressions are first derived. Then, a secrecy performance intelligent prediction algorithm is proposed. The extensive simulations are used to verify the proposed method. Compared with other methods, the proposed algorithm realizes a better prediction precision. Hindawi 2021-05-27 /pmc/articles/PMC8178003/ /pubmed/34136110 http://dx.doi.org/10.1155/2021/6265520 Text en Copyright © 2021 Fagen Yin et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yin, Fagen Xiao, Pingping Li, Zefeng ASC Performance Prediction for Medical IoT Communication Networks |
title | ASC Performance Prediction for Medical IoT Communication Networks |
title_full | ASC Performance Prediction for Medical IoT Communication Networks |
title_fullStr | ASC Performance Prediction for Medical IoT Communication Networks |
title_full_unstemmed | ASC Performance Prediction for Medical IoT Communication Networks |
title_short | ASC Performance Prediction for Medical IoT Communication Networks |
title_sort | asc performance prediction for medical iot communication networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178003/ https://www.ncbi.nlm.nih.gov/pubmed/34136110 http://dx.doi.org/10.1155/2021/6265520 |
work_keys_str_mv | AT yinfagen ascperformancepredictionformedicaliotcommunicationnetworks AT xiaopingping ascperformancepredictionformedicaliotcommunicationnetworks AT lizefeng ascperformancepredictionformedicaliotcommunicationnetworks |