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A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization

Fog-assisted and IoT-enabled smart healthcare system with rapid response rates is the major area of concern now a days. Dynamic and heterogeneous fog networks are difficult to manage and a considerable amount of overhead could be realized while managing ever increasing load on foglets. Fog computing...

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Autores principales: Abdellatif, Ahmed A. H., Singh, Aman, Aldribi, Abdulaziz, Ortega-Mansilla, Arturo, Ibrahim, Muhammad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9536960/
https://www.ncbi.nlm.nih.gov/pubmed/36210992
http://dx.doi.org/10.1155/2022/4174805
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author Abdellatif, Ahmed A. H.
Singh, Aman
Aldribi, Abdulaziz
Ortega-Mansilla, Arturo
Ibrahim, Muhammad
author_facet Abdellatif, Ahmed A. H.
Singh, Aman
Aldribi, Abdulaziz
Ortega-Mansilla, Arturo
Ibrahim, Muhammad
author_sort Abdellatif, Ahmed A. H.
collection PubMed
description Fog-assisted and IoT-enabled smart healthcare system with rapid response rates is the major area of concern now a days. Dynamic and heterogeneous fog networks are difficult to manage and a considerable amount of overhead could be realized while managing ever increasing load on foglets. Fog computing plays a vital role in managing ever increasing processing demands from diverse IoT-based applications. Smart healthcare systems work with the assistance of sensor-based devices and automatic data collection and processing can speed up overall system functionality. In the proposed work, a novel framework for smart health care is presented where a series of activities are performed with prime objective of reducing latency and execution time. Principal component analysis is used for feature reduction and support vector machines with radial basis function kernel is used for classification purpose. Workload optimization on the fog nodes is implemented using genetic algorithm. Data collection process also involves preprocessing as a leading step for generating cleaner data. Amalgamation of intelligent and optimization techniques in the presented framework certainly improves the efficiency of the overall system. Experimental results reveal that proposed work outperforms the existing fog-assisted smart healthcare systems in terms of latency, execution time, overall system accuracy, and system stability.
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spelling pubmed-95369602022-10-07 A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization Abdellatif, Ahmed A. H. Singh, Aman Aldribi, Abdulaziz Ortega-Mansilla, Arturo Ibrahim, Muhammad Comput Intell Neurosci Research Article Fog-assisted and IoT-enabled smart healthcare system with rapid response rates is the major area of concern now a days. Dynamic and heterogeneous fog networks are difficult to manage and a considerable amount of overhead could be realized while managing ever increasing load on foglets. Fog computing plays a vital role in managing ever increasing processing demands from diverse IoT-based applications. Smart healthcare systems work with the assistance of sensor-based devices and automatic data collection and processing can speed up overall system functionality. In the proposed work, a novel framework for smart health care is presented where a series of activities are performed with prime objective of reducing latency and execution time. Principal component analysis is used for feature reduction and support vector machines with radial basis function kernel is used for classification purpose. Workload optimization on the fog nodes is implemented using genetic algorithm. Data collection process also involves preprocessing as a leading step for generating cleaner data. Amalgamation of intelligent and optimization techniques in the presented framework certainly improves the efficiency of the overall system. Experimental results reveal that proposed work outperforms the existing fog-assisted smart healthcare systems in terms of latency, execution time, overall system accuracy, and system stability. Hindawi 2022-09-29 /pmc/articles/PMC9536960/ /pubmed/36210992 http://dx.doi.org/10.1155/2022/4174805 Text en Copyright © 2022 Ahmed A. H. Abdellatif 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
Abdellatif, Ahmed A. H.
Singh, Aman
Aldribi, Abdulaziz
Ortega-Mansilla, Arturo
Ibrahim, Muhammad
A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization
title A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization
title_full A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization
title_fullStr A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization
title_full_unstemmed A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization
title_short A Novel Framework for Fog-Assisted Smart Healthcare System with Workload Optimization
title_sort novel framework for fog-assisted smart healthcare system with workload optimization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9536960/
https://www.ncbi.nlm.nih.gov/pubmed/36210992
http://dx.doi.org/10.1155/2022/4174805
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