Smart teledentistry healthcare architecture for medical big data analysis using IoT-enabled environment

Babar, M; Tariq, MU; Alshehri, MD; Ullah, F; Uddin, MI

Smart teledentistry healthcare architecture for medical big data analysis using IoT-enabled environment

Babar, M Tariq, MU Alshehri, MD Ullah, F Uddin, MI

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Sustainable Computing: Informatics and Systems, 2022, 35, pp. 100719
Issue Date:: 2022-09-01

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Field	Value	Language
dc.contributor.author	Babar, M
dc.contributor.author	Tariq, MU
dc.contributor.author	Alshehri, MD
dc.contributor.author	Ullah, F
dc.contributor.author	Uddin, MI
dc.date.accessioned	2023-03-24T00:17:28Z
dc.date.available	2023-03-24T00:17:28Z
dc.date.issued	2022-09-01
dc.identifier.citation	Sustainable Computing: Informatics and Systems, 2022, 35, pp. 100719
dc.identifier.issn	2210-5379
dc.identifier.uri	http://hdl.handle.net/10453/168292
dc.description.abstract	The current spread out in Big Data analytics and the medical Internet of Things (IoT) originated the recognition of smart health. Smart health is the integration of devices, sensors, cameras, and objects or things embedded with sensors that generate an enormous amount of data known as big data. IoT-enabled smart health management assimilates the digital traces generated by things and humans including teledentistry. The key purpose of teledentistry is to analyze the data and produce valuable insights and hidden patterns to offer services and overcome the existing challenges. This article proposes a data management design using Big Data analytics for smart teledentistry planning. The proposed scheme is the customization of the present parallel platforms to accomplish effective processing. The design offers three different modules which are the data pre-processing, data ingestion, and data processing module. The pre-processing is performed to speed up the real data processing. The recent schemes lack well-organized and effective parallel data loading and proficient communication. The Big Data storage and ingestion are achieved using optimized utility and parallel mechanisms. Optimized RDD-enabled (resilient distribution) Yet Another Resource Negotiator (YARN) based proposal is designed for resourceful cluster administration and computation of medical Big Data. The proposed data design is experimentally simulated with authentic datasets using Apache Spark 3.0 framework. The proposed architecture is compared with existing state-of-the-art proposals. The simulation results reveal the efficacy of the proposed system.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Sustainable Computing: Informatics and Systems
dc.relation.isbasedon	10.1016/j.suscom.2022.100719
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Smart teledentistry healthcare architecture for medical big data analysis using IoT-enabled environment
dc.type	Journal Article
utslib.citation.volume	35
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-24T00:17:00Z
pubs.publication-status	Published
pubs.volume	35

Abstract:

The current spread out in Big Data analytics and the medical Internet of Things (IoT) originated the recognition of smart health. Smart health is the integration of devices, sensors, cameras, and objects or things embedded with sensors that generate an enormous amount of data known as big data. IoT-enabled smart health management assimilates the digital traces generated by things and humans including teledentistry. The key purpose of teledentistry is to analyze the data and produce valuable insights and hidden patterns to offer services and overcome the existing challenges. This article proposes a data management design using Big Data analytics for smart teledentistry planning. The proposed scheme is the customization of the present parallel platforms to accomplish effective processing. The design offers three different modules which are the data pre-processing, data ingestion, and data processing module. The pre-processing is performed to speed up the real data processing. The recent schemes lack well-organized and effective parallel data loading and proficient communication. The Big Data storage and ingestion are achieved using optimized utility and parallel mechanisms. Optimized RDD-enabled (resilient distribution) Yet Another Resource Negotiator (YARN) based proposal is designed for resourceful cluster administration and computation of medical Big Data. The proposed data design is experimentally simulated with authentic datasets using Apache Spark 3.0 framework. The proposed architecture is compared with existing state-of-the-art proposals. The simulation results reveal the efficacy of the proposed system.

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http://hdl.handle.net/10453/168292