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Agile Data Offloading over Novel Fog Computing Infrastructure for CAVs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Andrea Tassi
  • Ioannis Mavromatis
  • Robert Piechocki
  • Andrew Nix
  • Christian Compton
  • Tracey Poole
  • Wolfgang Schuster
Original languageEnglish
Title of host publication2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
Subtitle of host publicationVTC2019-Spring
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)9781728112176
ISBN (Print)978-1-7281-1218-3
DateAccepted/In press - 4 Mar 2019
DateE-pub ahead of print - 27 Jun 2019
DatePublished (current) - 27 Jun 2019
Event89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia
Duration: 28 Apr 20191 May 2019

Publication series

NameVehicular Technology Conference (VTC Spring)
ISSN (Print)1090-3038
ISSN (Electronic)2577-2465


Conference89th IEEE Vehicular Technology Conference, VTC Spring 2019
CityKuala Lumpur


Future Connected and Automated Vehicles (CAVs) will be supervised by cloud-based systems overseeing the overall security and orchestrating traffic flows. Such systems rely on data collected from CAVs across the whole city operational area. This paper develops a Fog Computing-based infrastructure for future Intelligent Transportation Systems (ITSs) enabling an agile and reliable off-load of CAV data. Since CAVs are expected to generate large quantities of data, it is not feasible to assume data off-loading to be completed while a CAV is in the proximity of a single Road-Side Unit (RSU). CAVs are expected to be in the range of an RSU only for a limited amount of time, necessitating data reconciliation across different RSUs, if traditional approaches to data off-load were to be used. To this end, this paper proposes an agile Fog Computing infrastructure, which interconnects all the RSUs so that the data reconciliation is solved efficiently as a by-product of deploying the Random Linear Network Coding (RLNC) technique. Our numerical results confirm the feasibility of our solution and show its effectiveness when operated in a large-scale urban testbed.

    Research areas

  • Fog computing, ITS, CAV, V2X, Network coding, Data offloading


89th IEEE Vehicular Technology Conference, VTC Spring 2019

Duration28 Apr 20191 May 2019
CityKuala Lumpur

Event: Conference

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 2.97 MB, PDF document


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