CWEmd: A light-weight Similarity Measurement for Resource Constraint Vehicular Networks

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dc.contributor.authorCheng Qiaoen
dc.contributor.authorKenneth N. Brownen
dc.contributor.authorYong Zhangen
dc.contributor.authorZhihong Tianen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderNational Natural Science Foundation of Chinaen
dc.contributor.funderChina Postdoctoral Science Foundationen
dc.contributor.funderBasic and Applied Basic Research Foundation of Guangdong Provinceen
dc.contributor.funderGuangzhou Universityen
dc.contributor.funderGuangzhou Cityen
dc.contributor.funderGuangdong Higher Education Innovation Groupen
dc.date.accessioned2023-06-22T13:17:03Z
dc.date.available2023-06-22T13:17:03Z
dc.date.issued2023-06-05en
dc.description.abstractGenerating an accurate machine learning (ML) model is of great importance for the Internet of Vehicles (IoV). However, obtaining such a model is challenging due to the fact that sub-groups of in-network vehicles receive data from different resources. A worthwhile investment then would be identifying those groups before inferring models. Similarity metrics are widely used to distinguish different groups. However, the efficiency of most existing similarity measurements is at the cost of increased computational complexity and decreased accuracy, making them unsuitable for IoV’s stringent conditions. To address this issue, we propose a computationally efficient method to measure the similarity of different vehicles, where a simplified version of Earth Mover’s Distance (EMD) is adopted. This distance metric is then embedded into a distributed clustering algorithm to learn the global pattern for vehicular systems. Our algorithm’s overall performance is measured using an Asynchronous Message Delay Simulator. Compared to the best algorithm of the state-of-the-art, our proposed algorithm converges slightly slower (by less than 1%) but improves the clustering accuracy by as much as 20% with synthetic data. Additionally, real-world data collected from Vehicles validates the efficiency of our proposed algorithm.en
dc.description.sponsorshipScience Foundation Ireland (SFI/12/RC/2289-P2); National Natural Science Foundation of China (Grant Nos. U20B2046; 62202114); China Postdoctoral Science Foundation (No. 2022M710861); Basic and Applied Basic Research Foundation of Guangdong Province (Nos.2020A1515010450; 202102020867); Basic Research Program Co-funded by Guangzhou City and Guangzhou University (No. 202102010445); Joint Research Fund of Guangzhou and University (Grant No. 202201020380); Guangdong Higher Education Innovation Group No.2020KCXTD007)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationQiao, C., Brown, K. N., Zhang, Y. and Tian, Z. (2023) 'CWEmd: A light-weight similarity measurement for resource constraint vehicular networks', IEEE Internet of Things Journal, 10(22), pp. 19655-19665. doi: 10.1109/JIOT.2023.3282968en
dc.identifier.doi10.1109/jiot.2023.3282968en
dc.identifier.eissn2327-4662en
dc.identifier.endpage19665
dc.identifier.issn2372-2541en
dc.identifier.issued22
dc.identifier.journaltitleIEEE Internet of Things Journalen
dc.identifier.startpage19655
dc.identifier.urihttps://hdl.handle.net/10468/14685
dc.identifier.volume10
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Spokes Programme::Fixed Call/16/SP/3804/IE/ENABLE: Connecting communities to smart urban environments through the Internet of Things/en
dc.rights© 2023, 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
dc.subjectInternet of Vehiclesen
dc.subjectEarth Mover’s Distanceen
dc.subjectSimilarity measurementen
dc.subjectDistributed algorithmen
dc.titleCWEmd: A light-weight Similarity Measurement for Resource Constraint Vehicular Networksen
dc.typeArticle (peer-reviewed)en
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