6g-enabled short-term forecasting for large-scale traffic flow in massive iot based on time-aware locality-sensitive hashing

Wang, F; Zhu, M; Wang, M; Khosravi, MR; Ni, Q; Yu, S; Qi, L

6g-enabled short-term forecasting for large-scale traffic flow in massive iot based on time-aware locality-sensitive hashing

Wang, F Zhu, M Wang, M Khosravi, MR Ni, Q Yu, S

Qi, L

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2021, 8, (7), pp. 5321-5331
Issue Date:: 2021-04-01

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Field	Value	Language
dc.contributor.author	Wang, F
dc.contributor.author	Zhu, M
dc.contributor.author	Wang, M
dc.contributor.author	Khosravi, MR
dc.contributor.author	Ni, Q
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.contributor.author	Qi, L
dc.date.accessioned	2021-04-28T06:56:15Z
dc.date.available	2021-04-28T06:56:15Z
dc.date.issued	2021-04-01
dc.identifier.citation	IEEE Internet of Things Journal, 2021, 8, (7), pp. 5321-5331
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/148472
dc.description.abstract	With the advent of the Internet of Things (IoT) and the increasing popularity of the intelligent transportation system, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data. Analyzing and mining such big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decision making often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and nonparametric model aided by 6G is proposed in this article to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware locality-sensitive hashing (LSH). We design a wide range of experiments based on a real-world big traffic data set to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2020.3037669
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.title	6g-enabled short-term forecasting for large-scale traffic flow in massive iot based on time-aware locality-sensitive hashing
dc.type	Journal Article
utslib.citation.volume	8
utslib.for	0805 Distributed Computing
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	2021-04-28T06:56:14Z
pubs.issue	7
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	7

Abstract:

With the advent of the Internet of Things (IoT) and the increasing popularity of the intelligent transportation system, a large number of sensing devices are installed on the road for monitoring traffic dynamics in real time. These sensors can collect streaming traffic data distributed across different traffic sites, which constitute the main source of big traffic data. Analyzing and mining such big traffic data in massive IoT can help traffic administrations to make scientific and reasonable traffic scheduling decisions, so as to avoid prospective traffic congestions in the future. However, the above traffic decision making often requires frequent and massive data transmissions between distributed sensors and centralized cloud computing centers, which calls for lightweight data integrations and accurate data analyses based on large-scale traffic data. In view of this challenge, a big data-driven and nonparametric model aided by 6G is proposed in this article to extract similar traffic patterns over time for accurate and efficient short-term traffic flow prediction in massive IoT, which is mainly based on time-aware locality-sensitive hashing (LSH). We design a wide range of experiments based on a real-world big traffic data set to validate the feasibility of our proposal. Experimental reports demonstrate that the prediction accuracy and efficiency of our proposal are increased by 32.6% and 97.3%, respectively, compared with the other two competitive approaches.

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http://hdl.handle.net/10453/148472