Memory-aware Online Compression of CAN Bus Data for Future Vehicular Systems

Niloofar Yazdani, Lars Nielsen, Daniel Enrique Lucani Rötter

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    Abstract

    Vehicles generate a large amount of data from their internal sensors. This data is not only useful for a vehicle's proper operation, but it provides car manufacturers with the ability to optimize the performance of individual vehicles and companies with fleets of vehicles (e.g., trucks, taxis, tractors) to optimize their operations to reduce fuel costs and plan repairs. This paper proposes algorithms to compress CAN bus data, specifically, packaged as MDF4 files. In particular, we propose lightweight, online and configurable compression algorithms that allow limited devices to choose the amount of RAM and flash memory allocated to them. We show that our proposals can outperform LZW for the same RAM footprint, and can even deliver comparable or better performance to DEFLATE under the same RAM limitations.

    OriginalsprogEngelsk
    Titel2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
    ForlagIEEE
    Publikationsdato2020
    Artikelnummer9348074
    ISBN (Elektronisk)978-1-7281-8298-8
    DOI
    StatusUdgivet - 2020
    Begivenhed2020 IEEE Global Communications Conference - Taipei, Taiwan + Online (Hybrid), Taipei, Taiwan
    Varighed: 7 dec. 202011 dec. 2020
    https://globecom2020.ieee-globecom.org/

    Konference

    Konference2020 IEEE Global Communications Conference
    LokationTaipei, Taiwan + Online (Hybrid)
    Land/OmrådeTaiwan
    ByTaipei
    Periode07/12/202011/12/2020
    Internetadresse
    NavnIEEE Global Communications Conference (GLOBECOM)
    ISSN1930-529X

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