QoE-Driven Big Data Architecture for Smart City

He, X; Wang, K; Huang, H; Liu, B

QoE-Driven Big Data Architecture for Smart City

He, X Wang, K Huang, H Liu, B

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Communications Magazine, 2018, 56, (2), pp. 88-93
Issue Date:: 2018-02-01

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Field	Value	Language
dc.contributor.author	He, X
dc.contributor.author	Wang, K
dc.contributor.author	Huang, H
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.date.accessioned	2022-09-03T07:32:25Z
dc.date.available	2022-09-03T07:32:25Z
dc.date.issued	2018-02-01
dc.identifier.citation	IEEE Communications Magazine, 2018, 56, (2), pp. 88-93
dc.identifier.issn	0163-6804
dc.identifier.issn	1558-1896
dc.identifier.uri	http://hdl.handle.net/10453/161266
dc.description.abstract	In the era of big data, the applications/services of the smart city are expected to offer end users better QoE than in a conventional smart city. Nevertheless, various types of sensors will produce an increasing volume of big data along with the implementation of a smart city, where we face redundant and diverse data. Therefore, providing satisfactory QoE will become the major challenge in the big-data-based smart city. In this article, to enhance the QoE, we propose a novel big data architecture consisting of three planes: The data storage plane, the data processing plane, and the data application plane. The data storage plane stores a wide variety of data collected by sensors and originating from different data sources. Then the data processing plane filters, analyzes, and processes the ocean of data to make decisions autonomously for extracting high-quality information. Finally, the application plane initiates the execution of the events corresponding to the decisions delivered from the data processing plane. Under this architecture, we particularly use machine learning techniques, trying to acquire accurate data and deliver precise information to end users. Simulation results indicate that our proposals could achieve high QoE performance for the smart city.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Communications Magazine
dc.relation.isbasedon	10.1109/MCOM.2018.1700231
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	QoE-Driven Big Data Architecture for Smart City
dc.type	Journal Article
utslib.citation.volume	56
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	closed_access	*
dc.date.updated	2022-09-03T07:32:24Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	56
utslib.citation.issue	2

Abstract:

In the era of big data, the applications/services of the smart city are expected to offer end users better QoE than in a conventional smart city. Nevertheless, various types of sensors will produce an increasing volume of big data along with the implementation of a smart city, where we face redundant and diverse data. Therefore, providing satisfactory QoE will become the major challenge in the big-data-based smart city. In this article, to enhance the QoE, we propose a novel big data architecture consisting of three planes: The data storage plane, the data processing plane, and the data application plane. The data storage plane stores a wide variety of data collected by sensors and originating from different data sources. Then the data processing plane filters, analyzes, and processes the ocean of data to make decisions autonomously for extracting high-quality information. Finally, the application plane initiates the execution of the events corresponding to the decisions delivered from the data processing plane. Under this architecture, we particularly use machine learning techniques, trying to acquire accurate data and deliver precise information to end users. Simulation results indicate that our proposals could achieve high QoE performance for the smart city.

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http://hdl.handle.net/10453/161266