Opportunistic fleets for road event detection in vehicular sensor networks

Zeng, Y; Li, D; Vasilakos, AV

Opportunistic fleets for road event detection in vehicular sensor networks

Zeng, Y Li, D Vasilakos, AV

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Wireless Networks, 2016, 22, (2), pp. 503-521
Issue Date:: 2016-02-01

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Field	Value	Language
dc.contributor.author	Zeng, Y
dc.contributor.author	Li, D
dc.contributor.author	Vasilakos, AV
dc.date.accessioned	2022-08-12T02:56:04Z
dc.date.available	2022-08-12T02:56:04Z
dc.date.issued	2016-02-01
dc.identifier.citation	Wireless Networks, 2016, 22, (2), pp. 503-521
dc.identifier.issn	1022-0038
dc.identifier.issn	1572-8196
dc.identifier.uri	http://hdl.handle.net/10453/160007
dc.description.abstract	High mobility and variable road surface condition brings big challenges for road event detection by using vehicular sensor networks (VSNs). VSNs are data-centric networks, so that the efficient collaborations among opportunistic topology structure in the network can help to improve the performance. In this paper, we present the behavior-aware fleet construction schemes to form and maintain the opportunistic fleet topology, and then to make fleet based data collaboration. The fleet formation scheme is to group vehicles according to behavior similarities and stability. The fleet maintenance scheme includes the periodic update and deputy selection to adapt the fleet topology. The collaboration within the fleet is executed in the way of “follower-to-leader” with data aggregation among behavior-aware vehicles. Our analysis show the schemes are with low overhead and in an on-line and decentralized way, which is well suited for VSNs. We made simulations to testify the scheme performance for road event detection applications. The results show that our schemes achieve better accuracy for road event detection when compared with the other related work.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Wireless Networks
dc.relation.isbasedon	10.1007/s11276-015-0976-2
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	Opportunistic fleets for road event detection in vehicular sensor networks
dc.type	Journal Article
utslib.citation.volume	22
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 Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-12T02:56:04Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	22
utslib.citation.issue	2

Abstract:

High mobility and variable road surface condition brings big challenges for road event detection by using vehicular sensor networks (VSNs). VSNs are data-centric networks, so that the efficient collaborations among opportunistic topology structure in the network can help to improve the performance. In this paper, we present the behavior-aware fleet construction schemes to form and maintain the opportunistic fleet topology, and then to make fleet based data collaboration. The fleet formation scheme is to group vehicles according to behavior similarities and stability. The fleet maintenance scheme includes the periodic update and deputy selection to adapt the fleet topology. The collaboration within the fleet is executed in the way of “follower-to-leader” with data aggregation among behavior-aware vehicles. Our analysis show the schemes are with low overhead and in an on-line and decentralized way, which is well suited for VSNs. We made simulations to testify the scheme performance for road event detection applications. The results show that our schemes achieve better accuracy for road event detection when compared with the other related work.

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http://hdl.handle.net/10453/160007