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A priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in Internet of Things
In this study, a priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in internet of things is proposed. In this model, patients’ data is labeled according to the proposed prioritizing mechanism. This provides a prioritized and delay-less servi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521385/ https://www.ncbi.nlm.nih.gov/pubmed/33013256 http://dx.doi.org/10.1016/j.pmcj.2020.101265 |
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author | Esmaeili, Sobhan Kamel Tabbakh, Seyed Reza Shakeri, Hassan |
author_facet | Esmaeili, Sobhan Kamel Tabbakh, Seyed Reza Shakeri, Hassan |
author_sort | Esmaeili, Sobhan |
collection | PubMed |
description | In this study, a priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in internet of things is proposed. In this model, patients’ data is labeled according to the proposed prioritizing mechanism. This provides a prioritized and delay-less service in the server side for the patients with critical conditions. In the proposed model, the sensed data is monitored in a real time way to calculate its sparsity level. Then, the ,calculated sparsity level is used to determine the number of required measurements for data sampling. This allows to sample the data with the number of measurements proportional to the sparsity level and information content of the data. Moreover, the particular design of the measurement matrix causes the aggregated data to be encrypted and its security be guaranteed. Simulation results show that compared to its counterpart schemes, the proposed sensing model not only provides security but also reduces the average energy consumption of the sensor nodes and the average packet delivery delay. This improvement originates from the reduction of the number of required bits for transferring the sensed data and is due to the consideration of the information content and sparsity level variation in the sensed data. |
format | Online Article Text |
id | pubmed-7521385 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75213852020-09-29 A priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in Internet of Things Esmaeili, Sobhan Kamel Tabbakh, Seyed Reza Shakeri, Hassan Pervasive Mob Comput Article In this study, a priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in internet of things is proposed. In this model, patients’ data is labeled according to the proposed prioritizing mechanism. This provides a prioritized and delay-less service in the server side for the patients with critical conditions. In the proposed model, the sensed data is monitored in a real time way to calculate its sparsity level. Then, the ,calculated sparsity level is used to determine the number of required measurements for data sampling. This allows to sample the data with the number of measurements proportional to the sparsity level and information content of the data. Moreover, the particular design of the measurement matrix causes the aggregated data to be encrypted and its security be guaranteed. Simulation results show that compared to its counterpart schemes, the proposed sensing model not only provides security but also reduces the average energy consumption of the sensor nodes and the average packet delivery delay. This improvement originates from the reduction of the number of required bits for transferring the sensed data and is due to the consideration of the information content and sparsity level variation in the sensed data. Elsevier B.V. 2020-11 2020-09-28 /pmc/articles/PMC7521385/ /pubmed/33013256 http://dx.doi.org/10.1016/j.pmcj.2020.101265 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Esmaeili, Sobhan Kamel Tabbakh, Seyed Reza Shakeri, Hassan A priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in Internet of Things |
title | A priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in Internet of Things |
title_full | A priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in Internet of Things |
title_fullStr | A priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in Internet of Things |
title_full_unstemmed | A priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in Internet of Things |
title_short | A priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in Internet of Things |
title_sort | priority-aware lightweight secure sensing model for body area networks with clinical healthcare applications in internet of things |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7521385/ https://www.ncbi.nlm.nih.gov/pubmed/33013256 http://dx.doi.org/10.1016/j.pmcj.2020.101265 |
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