Mobile crowdsourcing: Framework, challenges, and solutions

Yufeng Wang, Xueyu Jia, Qun Jin, Jianhua Ma

    Research output: Contribution to journalArticle

    23 Citations (Scopus)

    Abstract

    Crowdsourcing is the generalized act of outsourcing tasks, traditionally performed by an employee or contractor, to a large group of Internet population through an open call. With the great development of smartphones with rich built-in sensors and multiple ratio interfaces, mixing smartphone-based mobile technologies and crowdsourcing offers significant flexibilities and leads to a new paradigm called mobile crowdsourcing (MCS), which can be fully explored for real-time and location-sensitive crowdsourced tasks. In this paper, we present a taxonomy for the MCS applications, which are explicitly divided as using human as sensors, and exploiting the wisdom of crowd (i.e., human intelligence). Moreover, two paradigms for mobilizing users in MCS are outlined: direct mode and word of mouth mode. A comprehensive MCS framework and typical workflow of MCS applications are proposed, which consist of nine functional modules, pertaining to three stakeholders in MCS: crowdsourcer, crowdworkers, and crowdsourcing platform. Then, we elaborate the MCS challenges including task management, incentives, security and privacy, and quality control, and summarize the corresponding solutions. Especially, from the viewpoints of various stakeholders, we propose the desired properties that an ideal MCS system should satisfy. The primary goal of this paper is to comprehensively classify and provide a summary on MCS framework, challenges, and possible solutions to highlight the MCS related research topics and facilitate to develop and deploy interesting MCS applications.

    Original languageEnglish
    JournalConcurrency Computation
    DOIs
    Publication statusAccepted/In press - 2016

    Fingerprint

    Mobile Applications
    Paradigm
    Mobile Technology
    Sensor
    Outsourcing
    Smartphones
    Mobile Systems
    Quality Control
    Taxonomy
    Framework
    Crowdsourcing
    Incentives
    Work Flow
    Privacy
    Flexibility
    Classify
    Real-time
    Module
    Sensors
    Taxonomies

    Keywords

    • Incentive mechanisms
    • Mobile crowdsourcing (MCS)
    • Quality control
    • Word of mouth (WoM)

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Computer Science Applications
    • Software
    • Computational Theory and Mathematics
    • Theoretical Computer Science

    Cite this

    Mobile crowdsourcing : Framework, challenges, and solutions. / Wang, Yufeng; Jia, Xueyu; Jin, Qun; Ma, Jianhua.

    In: Concurrency Computation, 2016.

    Research output: Contribution to journalArticle

    @article{c6193b44f40f477e9d730b99a493c4c8,
    title = "Mobile crowdsourcing: Framework, challenges, and solutions",
    abstract = "Crowdsourcing is the generalized act of outsourcing tasks, traditionally performed by an employee or contractor, to a large group of Internet population through an open call. With the great development of smartphones with rich built-in sensors and multiple ratio interfaces, mixing smartphone-based mobile technologies and crowdsourcing offers significant flexibilities and leads to a new paradigm called mobile crowdsourcing (MCS), which can be fully explored for real-time and location-sensitive crowdsourced tasks. In this paper, we present a taxonomy for the MCS applications, which are explicitly divided as using human as sensors, and exploiting the wisdom of crowd (i.e., human intelligence). Moreover, two paradigms for mobilizing users in MCS are outlined: direct mode and word of mouth mode. A comprehensive MCS framework and typical workflow of MCS applications are proposed, which consist of nine functional modules, pertaining to three stakeholders in MCS: crowdsourcer, crowdworkers, and crowdsourcing platform. Then, we elaborate the MCS challenges including task management, incentives, security and privacy, and quality control, and summarize the corresponding solutions. Especially, from the viewpoints of various stakeholders, we propose the desired properties that an ideal MCS system should satisfy. The primary goal of this paper is to comprehensively classify and provide a summary on MCS framework, challenges, and possible solutions to highlight the MCS related research topics and facilitate to develop and deploy interesting MCS applications.",
    keywords = "Incentive mechanisms, Mobile crowdsourcing (MCS), Quality control, Word of mouth (WoM)",
    author = "Yufeng Wang and Xueyu Jia and Qun Jin and Jianhua Ma",
    year = "2016",
    doi = "10.1002/cpe.3789",
    language = "English",
    journal = "Concurrency Computation Practice and Experience",
    issn = "1532-0626",
    publisher = "John Wiley and Sons Ltd",

    }

    TY - JOUR

    T1 - Mobile crowdsourcing

    T2 - Framework, challenges, and solutions

    AU - Wang, Yufeng

    AU - Jia, Xueyu

    AU - Jin, Qun

    AU - Ma, Jianhua

    PY - 2016

    Y1 - 2016

    N2 - Crowdsourcing is the generalized act of outsourcing tasks, traditionally performed by an employee or contractor, to a large group of Internet population through an open call. With the great development of smartphones with rich built-in sensors and multiple ratio interfaces, mixing smartphone-based mobile technologies and crowdsourcing offers significant flexibilities and leads to a new paradigm called mobile crowdsourcing (MCS), which can be fully explored for real-time and location-sensitive crowdsourced tasks. In this paper, we present a taxonomy for the MCS applications, which are explicitly divided as using human as sensors, and exploiting the wisdom of crowd (i.e., human intelligence). Moreover, two paradigms for mobilizing users in MCS are outlined: direct mode and word of mouth mode. A comprehensive MCS framework and typical workflow of MCS applications are proposed, which consist of nine functional modules, pertaining to three stakeholders in MCS: crowdsourcer, crowdworkers, and crowdsourcing platform. Then, we elaborate the MCS challenges including task management, incentives, security and privacy, and quality control, and summarize the corresponding solutions. Especially, from the viewpoints of various stakeholders, we propose the desired properties that an ideal MCS system should satisfy. The primary goal of this paper is to comprehensively classify and provide a summary on MCS framework, challenges, and possible solutions to highlight the MCS related research topics and facilitate to develop and deploy interesting MCS applications.

    AB - Crowdsourcing is the generalized act of outsourcing tasks, traditionally performed by an employee or contractor, to a large group of Internet population through an open call. With the great development of smartphones with rich built-in sensors and multiple ratio interfaces, mixing smartphone-based mobile technologies and crowdsourcing offers significant flexibilities and leads to a new paradigm called mobile crowdsourcing (MCS), which can be fully explored for real-time and location-sensitive crowdsourced tasks. In this paper, we present a taxonomy for the MCS applications, which are explicitly divided as using human as sensors, and exploiting the wisdom of crowd (i.e., human intelligence). Moreover, two paradigms for mobilizing users in MCS are outlined: direct mode and word of mouth mode. A comprehensive MCS framework and typical workflow of MCS applications are proposed, which consist of nine functional modules, pertaining to three stakeholders in MCS: crowdsourcer, crowdworkers, and crowdsourcing platform. Then, we elaborate the MCS challenges including task management, incentives, security and privacy, and quality control, and summarize the corresponding solutions. Especially, from the viewpoints of various stakeholders, we propose the desired properties that an ideal MCS system should satisfy. The primary goal of this paper is to comprehensively classify and provide a summary on MCS framework, challenges, and possible solutions to highlight the MCS related research topics and facilitate to develop and deploy interesting MCS applications.

    KW - Incentive mechanisms

    KW - Mobile crowdsourcing (MCS)

    KW - Quality control

    KW - Word of mouth (WoM)

    UR - http://www.scopus.com/inward/record.url?scp=84959080429&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84959080429&partnerID=8YFLogxK

    U2 - 10.1002/cpe.3789

    DO - 10.1002/cpe.3789

    M3 - Article

    AN - SCOPUS:84959080429

    JO - Concurrency Computation Practice and Experience

    JF - Concurrency Computation Practice and Experience

    SN - 1532-0626

    ER -