A Data Stream Cleaning System Using Edge Intelligence for Smart City Industrial Environments

Sun, D; Xue, S; Wu, H; Wu, J

A Data Stream Cleaning System Using Edge Intelligence for Smart City Industrial Environments

Sun, D Xue, S Wu, H Wu, J

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Informatics, 2022, 18, (2), pp. 1165-1174
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Sun, D
dc.contributor.author	Xue, S
dc.contributor.author	Wu, H
dc.contributor.author	Wu, J
dc.date.accessioned	2022-06-03T03:08:19Z
dc.date.available	2022-06-03T03:08:19Z
dc.date.issued	2022-02-01
dc.identifier.citation	IEEE Transactions on Industrial Informatics, 2022, 18, (2), pp. 1165-1174
dc.identifier.issn	1551-3203
dc.identifier.issn	1941-0050
dc.identifier.uri	http://hdl.handle.net/10453/157895
dc.description.abstract	Cities are becoming smarter because of recent advances in artificial intelligence and the Internet of Things. However, heterogeneous data source in smart cities are continuously producing low-quality data, and ever-growing applications have greater real-time requirements. Therefore, this article proposes a data stream cleaning system (named DSCS) using edge intelligence to utilize the advantages of cloud servers and edge devices. The DSCS in edge nodes consists of a dynamic protocol interpreter, a structure parser, and a cleaning model activator. Meanwhile, a cloud server, which has pools of protocol and structured programs and cleaning models, supports the edge nodes to adapt massive heterogeneous data sources. To validate the proposed data cleaning system, we applied it to two scenarios: monitoring the injection molding machines, and base stations. The DSCS can have a stable processing time when the number of accessed edge devices is increased, as well as a good cleaning effect.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Industrial Informatics
dc.relation.isbasedon	10.1109/TII.2021.3077865
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Electrical & Electronic Engineering
dc.title	A Data Stream Cleaning System Using Edge Intelligence for Smart City Industrial Environments
dc.type	Journal Article
utslib.citation.volume	18
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-06-03T03:08:17Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	2

Abstract:

Cities are becoming smarter because of recent advances in artificial intelligence and the Internet of Things. However, heterogeneous data source in smart cities are continuously producing low-quality data, and ever-growing applications have greater real-time requirements. Therefore, this article proposes a data stream cleaning system (named DSCS) using edge intelligence to utilize the advantages of cloud servers and edge devices. The DSCS in edge nodes consists of a dynamic protocol interpreter, a structure parser, and a cleaning model activator. Meanwhile, a cloud server, which has pools of protocol and structured programs and cleaning models, supports the edge nodes to adapt massive heterogeneous data sources. To validate the proposed data cleaning system, we applied it to two scenarios: monitoring the injection molding machines, and base stations. The DSCS can have a stable processing time when the number of accessed edge devices is increased, as well as a good cleaning effect.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157895