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Publication - Dr Ioannis Mavromatis

    Secure Data Offloading Strategy for Connected and Autonomous Vehicles

    Citation

    Tassi, A, Mavromatis, I, Piechocki, RJ & Nix, A, 2019, ‘Secure Data Offloading Strategy for Connected and Autonomous Vehicles’. in: 2019 IEEE 89th Vehicular Technology Conference, VTC Spring 2019 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE)

    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.

    Full details in the University publications repository