IoT-Enabled Big Data Analytics Architecture for Multimedia Data Communications

Babar, M; Alshehri, MD; Tariq, MU; Ullah, F; Khan, A; Uddin, MI; Almasoud, AS

IoT-Enabled Big Data Analytics Architecture for Multimedia Data Communications

Babar, M Alshehri, MD Tariq, MU Ullah, F Khan, A Uddin, MI Almasoud, AS

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Publisher:: Hindawi Limited
Publication Type:: Journal Article
Citation:: Wireless Communications and Mobile Computing, 2021, 2021, pp. 1-9
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Babar, M
dc.contributor.author	Alshehri, MD
dc.contributor.author	Tariq, MU
dc.contributor.author	Ullah, F
dc.contributor.author	Khan, A
dc.contributor.author	Uddin, MI
dc.contributor.author	Almasoud, AS
dc.date.accessioned	2022-05-06T05:54:25Z
dc.date.available	2022-05-06T05:54:25Z
dc.date.issued	2021-01-01
dc.identifier.citation	Wireless Communications and Mobile Computing, 2021, 2021, pp. 1-9
dc.identifier.issn	1530-8669
dc.identifier.issn	1530-8677
dc.identifier.uri	http://hdl.handle.net/10453/157091
dc.description.abstract	The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia-enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT-enabled data management systems to handle MMBD including high-volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision-making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT-enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real-time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture.
dc.language	en
dc.publisher	Hindawi Limited
dc.relation.ispartof	Wireless Communications and Mobile Computing
dc.relation.isbasedon	10.1155/2021/5283309
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	IoT-Enabled Big Data Analytics Architecture for Multimedia Data Communications
dc.type	Journal Article
utslib.citation.volume	2021
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
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	open_access	*
dc.date.updated	2022-05-06T05:54:20Z
pubs.publication-status	Published
pubs.volume	2021

Abstract:

The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia-enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT-enabled data management systems to handle MMBD including high-volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision-making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT-enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real-time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture.

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http://hdl.handle.net/10453/157091