Vehicular Fog Computing for Video Crowdsourcing

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Zhu, Chao
dc.contributor.author Pastor, Giancarlo
dc.contributor.author Xiao, Yu
dc.contributor.author Ylajaaski, Antti
dc.date.accessioned 2018-11-09T13:06:29Z
dc.date.available 2018-11-09T13:06:29Z
dc.date.issued 2018-10-01
dc.identifier.citation Zhu , C , Pastor , G , Xiao , Y & Ylajaaski , A 2018 , ' Vehicular Fog Computing for Video Crowdsourcing : Applications, Feasibility, and Challenges ' IEEE Communications Magazine , vol 56 , no. 10 , 8493119 , pp. 58-63 . DOI: 10.1109/MCOM.2018.1800116 en
dc.identifier.issn 0163-6804
dc.identifier.issn 1558-1896
dc.identifier.other PURE UUID: 6ea522db-ceab-4ceb-b429-bb00bbd4f3b5
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/vehicular-fog-computing-for-video-crowdsourcing(6ea522db-ceab-4ceb-b429-bb00bbd4f3b5).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85055315380&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/29317369/ELEC_Zhu_et_al_vehicular_fog_IEEECM.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/34616
dc.description | openaire: EC/H2020/815191/EU//PriMO-5G
dc.description.abstract With the growing adoption of dash cameras, we are seeing great potential for innovations by analyzing the video collected from vehicles. On the other hand, transmitting and analyzing a large amount of video, especially high-resolution video in real time, requires a lot of communications and computing resources. In this work, we investigate the feasibility and challenges of applying vehicular fog computing for real- time analytics of crowdsourced dash camera video. Instead of forwarding all the video to the cloud, we propose to turn commercial fleets (e.g., buses and taxis) into vehicular fog nodes, and to utilize these nodes to gather and process the video from the vehicles within communication ranges. We assess the feasibility of our proposal in two steps. First, we analyze the availability of vehicular fog nodes based on a real-world traffic dataset. Second, we explore the serviceability of vehicular fog nodes by evaluating the networking performance of fog-enabled video crowdsourcing over two mainstream access technologies, DSRC and LTE. Based on our findings, we also summarize the challenges to largescale real-time analytics of crowdsourced videos over vehicular networks. en
dc.format.extent 6
dc.format.extent 58-63
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation info:eu-repo/grantAgreement/EC/H2020/815191/EU//PriMO-5G
dc.relation.ispartofseries IEEE Communications Magazine en
dc.relation.ispartofseries Volume 56, issue 10 en
dc.rights openAccess en
dc.subject.other Computer Science Applications en
dc.subject.other Computer Networks and Communications en
dc.subject.other Electrical and Electronic Engineering en
dc.subject.other 113 Computer and information sciences en
dc.title Vehicular Fog Computing for Video Crowdsourcing en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.contributor.department Department of Communications and Networking
dc.subject.keyword Computer Science Applications
dc.subject.keyword Computer Networks and Communications
dc.subject.keyword Electrical and Electronic Engineering
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201811095657
dc.identifier.doi 10.1109/MCOM.2018.1800116
dc.type.version acceptedVersion


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