mca 13(3): e6

Research Article

Scalable Stream Processing with Quality of Service for Smart City Crowdsensing Applications

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  • @ARTICLE{10.4108/mca.1.3.e6,
        author={Paolo Bellavista and Antonio Corradi and Andrea Reale},
        title={Scalable Stream Processing with Quality of Service for Smart City Crowdsensing Applications},
        journal={EAI Endorsed Transactions on Mobile Communications and Applications},
        volume={1},
        number={3},
        publisher={ICST},
        journal_a={MCA},
        year={2013},
        month={12},
        keywords={},
        doi={10.4108/mca.1.3.e6}
    }
    
  • Paolo Bellavista
    Antonio Corradi
    Andrea Reale
    Year: 2013
    Scalable Stream Processing with Quality of Service for Smart City Crowdsensing Applications
    MCA
    ICST
    DOI: 10.4108/mca.1.3.e6
Paolo Bellavista1,*, Antonio Corradi1,*, Andrea Reale1,*
  • 1: DISI - University of Bologna, Italy
*Contact email: paolo.bellavista@unibo.it, antonio.corradi@unibo.it, andrea.reale@unibo.it

Abstract

Crowdsensing is emerging as a powerful paradigm capable of leveraging the collective, though imprecise, monitoring capabilities of common people carrying smartphones or other personal devices, which can effectively become real-time mobile sensors, collecting information about the physical places they live in. This unprecedented amount of information, considered collectively, offers new valuable opportunities to understand more thoroughly the environment in which we live and, more importantly, gives the chance to use this deeper knowledge to act and improve, in a virtuous loop, the environment itself. However, managing this process is a hard technical challenge, spanning several socio-technical issues: here, we focus on the related quality, reliability, and scalability trade-offs by proposing an architecture for crowdsensing platforms that dynamically self-configure and self-adapt depending on application-specific quality requirements. In the context of this general architecture, the paper will specifically focus on the Quasit distributed stream processing middleware, and show how Quasit can be used to process and analyze crowdsensing-generated data flows with differentiated quality requirements in a highly scalable and reliable way.