Secure Data Offloading Strategy for Connected and Autonomous Vehicles

Andrea Tassi, Ioannis Mavromatis, Robert J. Piechocki, Andrew Nix

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

5 Citations (Scopus)
162 Downloads (Pure)


Connected and Automated Vehicles (CAVs) are expected to constantly interact with a network of processing nodes installed in secure cabinets located at the side of the road -- thus, forming Fog Computing-based infrastructure for Intelligent Transportation Systems (ITSs). Future city-scale ITS services will heavily rely upon the sensor data regularly off-loaded by each CAV on the Fog Computing network. Due to the broadcast nature of the medium, CAVs' communications can be vulnerable to eavesdropping. This paper proposes a novel data offloading approach where the Random Linear Network Coding (RLNC) principle is used to ensure the probability of an eavesdropper to recover relevant portions of sensor data is minimized. Our preliminary results confirm the effectiveness of our approach when operated in a large-scale ITS networks.
Original languageEnglish
Title of host publication2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages2
ISBN (Electronic)9781728112176
ISBN (Print)9781728112183
Publication statusPublished - 27 Jun 2019
Event89th IEEE Vehicular Technology Conference, VTC Spring 2019 - Kuala Lumpur, Malaysia
Duration: 28 Apr 20191 May 2019

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference89th IEEE Vehicular Technology Conference, VTC Spring 2019
CityKuala Lumpur

Bibliographical note

To appear in IEEE VTC-Spring 2019


  • V2X
  • CAV
  • ITS
  • Fog Computing
  • Data Offloading
  • Secrecy Outage Probability
  • Intercept Probability


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