A mobile positioning method based on integrated heterogeneous networks for commercial vehicle operation systems

Liu, G; Guo, C; Chen, C-H; Guo, W; Hwang, FJ

A mobile positioning method based on integrated heterogeneous networks for commercial vehicle operation systems

Liu, G Guo, C Chen, C-H Guo, W Hwang, FJ

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE International Conference on Industrial Internet, 2020, 00, pp. 393-398
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Liu, G
dc.contributor.author	Guo, C
dc.contributor.author	Chen, C-H
dc.contributor.author	Guo, W
dc.contributor.author	Hwang, FJ
dc.date	2019-11-11
dc.date.accessioned	2021-02-17T22:45:11Z
dc.date.available	2019-08-11
dc.date.available	2021-02-17T22:45:11Z
dc.date.issued	2020
dc.identifier.citation	Proceedings of the IEEE International Conference on Industrial Internet, 2020, 00, pp. 393-398
dc.identifier.isbn	978-1-7281-2977-8
dc.identifier.uri	http://hdl.handle.net/10453/146189
dc.description.abstract	Due to the growing demand for logistics, the commercial vehicle operation (CVO) systems have been a popular application of intelligent transportation systems. Determining the locations of on-board units (OBUs) precisely is an important issue for managing cargo transportation processes. Therefore, this study proposes a mobile positioning method based on integrated heterogeneous networks to analyze the historical location information and network signals which are obtained from CVO systems. This method mainly analyzes the network signals from heterogeneous networks (e.g. the cellular networks and WiFi networks) and considers the factors of received signal strength indicators, time series data, and transmission distance for mobile position. In practical experiments, the results show that the mean location error of the proposed method is 3.36m lower than other methods. Therefore, it is feasible to adopt the proposed method for determining the location of OBU for the improvement of CVO system.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the IEEE International Conference on Industrial Internet
dc.relation.ispartof	IEEE International Conference on Industrial Internet
dc.relation.isbasedon	10.1109/ICII.2019.00073
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A mobile positioning method based on integrated heterogeneous networks for commercial vehicle operation systems
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Orlando, USA
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2021-02-17T22:45:03Z
pubs.finish-date	2019-11-12
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-11-11
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Due to the growing demand for logistics, the commercial vehicle operation (CVO) systems have been a popular application of intelligent transportation systems. Determining the locations of on-board units (OBUs) precisely is an important issue for managing cargo transportation processes. Therefore, this study proposes a mobile positioning method based on integrated heterogeneous networks to analyze the historical location information and network signals which are obtained from CVO systems. This method mainly analyzes the network signals from heterogeneous networks (e.g. the cellular networks and WiFi networks) and considers the factors of received signal strength indicators, time series data, and transmission distance for mobile position. In practical experiments, the results show that the mean location error of the proposed method is 3.36m lower than other methods. Therefore, it is feasible to adopt the proposed method for determining the location of OBU for the improvement of CVO system.

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http://hdl.handle.net/10453/146189