Automakers manage vast fleets of connected vehicles and face an ever-increasing demand for their sensor readings. This demand originates from many stakeholders, each potentially requiring different sensors from different vehicles. Currently, this demand remains largely unfulfilled due to a lack of systems that can handle such diverse demands efficiently. Vehicles are usually passive participants in data acquisition, each continuously reading and transmitting the same static set of sensors. However, in a multi-tenant setup with diverse data demands, each vehicle potentially needs to provide different data instead. We present a system that performs such vehicle-specific minimization of data acquisition by mapping individual data demands to individual vehicles. We collect personal data only after prior consent and fulfill the requirements of the GDPR. Non-personal data can be collected by directly addressing individual vehicles. The system consists of a software component natively integrated with a major automaker’s vehicle platform and a cloud platform brokering access to acquired data. Sensor readings are either provided via near real-time streaming or as recorded trip files that provide specific consistency guarantees. A performance evaluation with over 200,000 simulated vehicles has shown that our system can increase server capacity on-demand and process streaming data within 269 ms on average during peak load. The resulting architecture can be used by other automakers or operators of large sensor networks. Native vehicle integration is not mandatory; the architecture can also be used with retrofitted hardware such as OBD readers. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.


    Access

    Download


    Export, share and cite



    Title :

    Demand-driven data acquisition for large scale fleets


    Contributors:

    Publication date :

    2021-01-01


    Remarks:

    Sensors 21 (2021), Nr. 21 ; Sensors



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    620 / 629




    Demand-driven data acquisition for large scale fleets

    Matesanz, Philip / Graen, Timo / Fiege, Andrea et al. | TIBKAT | 2021

    Free access

    Dynamic demand modeling of freight fleets

    Kühn, André / Krail, Michael | Fraunhofer Publica | 2013

    Free access

    Joint Session: Passenger Demand and Fleets/Manufacturers

    Diamond, M. / Transportation Research Board / United States | British Library Conference Proceedings | 1998


    Battery electric vehicle acquisition timeframes in Canadian fleets

    Khan, Shakil / Maoh, Hanna | Taylor & Francis Verlag | 2021


    Towards Detection of Road Weather Conditions using Large-Scale Vehicle Fleets

    Mercelis, Siegfried / Watelet, Sylvain / Casteels, Wim et al. | IEEE | 2020