@inproceedings{88f620e5d9c04fb5bb84550855027317,
title = "Secure Data Offloading Strategy for Connected and Autonomous Vehicles",
abstract = " 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. ",
keywords = "V2X, CAV, ITS, Fog Computing, Data Offloading, Secrecy Outage Probability, Intercept Probability",
author = "Andrea Tassi and Ioannis Mavromatis and Piechocki, {Robert J.} and Andrew Nix",
note = "To appear in IEEE VTC-Spring 2019; 89th IEEE Vehicular Technology Conference, VTC Spring 2019 ; Conference date: 28-04-2019 Through 01-05-2019",
year = "2019",
month = jun,
day = "27",
doi = "10.1109/VTCSpring.2019.8746698",
language = "English",
isbn = "9781728112183",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings",
address = "United States",
}