Skip to content

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
DOIs
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)
PublisherIEEE
ISSN (Print)1090-3038
ISSN (Electronic)2577-2465

Conference

Conference89th IEEE Vehicular Technology Conference, VTC Spring 2019
CountryMalaysia
CityKuala Lumpur
Period28/04/191/05/19

Abstract

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

Event

89th IEEE Vehicular Technology Conference, VTC Spring 2019

Duration28 Apr 20191 May 2019
CityKuala Lumpur
CountryMalaysia

Event: Conference

Download statistics

No data available

Documents

Documents

  • 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 https://ieeexplore.ieee.org/document/8746302. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 2.97 MB, PDF document

DOI

View research connections

Related faculties, schools or groups