The emergence of vehicular-Ad-Hoc network and Vehicular Cloud Computing bring about potential for building powerful vehicular crowd-sensing system. Current research for modern crowd-sensing focuses on large scale applications such as urban sensing, public safety, traffic or environmental monitoring for governments or enterprises. Thus, to the best of our knowledge, there is no effort on extending crowd-sensing to small scale personalized tasks. A major challenge in small scale crowd-sensing is to achieve maximum sensor coverage while minimizing the set of vehicle necessary. In this paper, we propose a novel vehicular recruitment scheme to support vehicular client crowd-sensing tasks, which are various, time sensitive and often limited in budget. Our simulation results, based on the large scale vehicular mobility dataset show that our proposed solution is efficient at minimizing required vehicular participants and ensuring data timeliness.


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    Titel :

    GoSense: Efficient Vehicle Selection for User Defined Vehicular Crowdsensing


    Beteiligte:
    Yu, Tzu-Yang (Autor:in) / Zhu, Xiru (Autor:in) / Chen, Hongji (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    734896 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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