Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89207
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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.creatorHo, IWHen_US
dc.creatorChau, SCKen_US
dc.creatorMagsino, ERen_US
dc.creatorJia, Ken_US
dc.date.accessioned2021-02-18T09:15:05Z-
dc.date.available2021-02-18T09:15:05Z-
dc.identifier.issn0018-9545en_US
dc.identifier.urihttp://hdl.handle.net/10397/89207-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.en_US
dc.rightsThe following publication Ho, I. W. H., Chau, S. C. K., Magsino, E. R., & Jia, K. (2019). Efficient 3D road map data exchange for intelligent vehicles in vehicular fog networks. IEEE Transactions on Vehicular Technology, 69(3), 3151-3165, is available at https://dx.doi.org/10.1109/TVT.2019.2963346.en_US
dc.subjectFog computingen_US
dc.subjectIndex codingen_US
dc.subjectIntelligent connected vehiclesen_US
dc.subjectOpportunistic schedulingen_US
dc.subjectVehicular networksen_US
dc.titleEfficient 3D road map data exchange for intelligent vehicles in vehicular fog networksen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage3151en_US
dc.identifier.epage3165en_US
dc.identifier.volume69en_US
dc.identifier.issue3en_US
dc.identifier.doi10.1109/TVT.2019.2963346en_US
dcterms.abstractThrough connecting intelligent vehicles as well as the roadside infrastructure, the perception range of vehicles can be significantly extended, and hidden objects at blind spots can be efficiently detected and avoided. To realize this, accurate road map data must be downloaded in real time to these intelligent vehicles for navigation and localization purposes. Besides, the cloud must be updated with dynamic changes that happened in the road network. These involve the transmissions of high-definition 3D road map data for accurately representing the physical environments. In this work, we propose solutions under the fog computing architecture in a heterogeneous vehicular network to optimize data exchange among intelligent vehicles, the roadside infrastructure, as well as regional databases. Specifically, the efficiency of 3D road map data dissemination at roadside fog nodes is achieved by exploiting index coding techniques to reduce the overall data load, while opportunistic scheduling of heterogeneous transmissions can be done to judiciously manage network resources and minimize operating cost. In addition, 3D point cloud coding and hashing techniques are applied to expedite the updates of various dynamic changes in the network. We empirically evaluate the proposed solutions based on real-world mobility traces of vehicles and 3D LIght Detection And Ranging (LIDAR) data of city streets. The proposed system is also implemented in a multi-robotic testbed for practical evaluation.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on vehicular technology, Mar 2020, v. 69, no. 3, 8946549, p. 3151-3165en_US
dcterms.isPartOfIEEE transactions on vehicular technologyen_US
dcterms.issued2020-03-
dc.identifier.scopus2-s2.0-85082045993-
dc.identifier.eissn1939-9359en_US
dc.identifier.artn8946549en_US
dc.description.validate202102 bcwhen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0578-n02-
dc.identifier.SubFormID274-
dc.description.fundingSourceRGCen_US
dc.description.fundingText15201118en_US
dc.description.pubStatusPublisheden_US
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