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A Cloud-Based Infrastructure for Crowdsourcing Data from Mobile Devices

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Crowdsourcing

Part of the book series: Progress in IS ((PROIS))

Abstract

In the vast galaxy of crowdsourcing activities, crowd-sensing consists in using users’ cellphones for collecting large sets of data. In this chapter, we present the APISENSE distributed crowd-sensing platform. In particular, APISENSE provides a participative environment to easily deploy sensing experiments in the wild. Beyond the scientific contributions of this platform, the technical originality of APISENSE lies in its Cloud orientation, which is built on top of the soCloud distributed multi-cloud platform, and the remote deployment of scripts within the mobile devices of the participants. We validate this solution by reporting on various crowd-sensing experiments we deployed using Android smartphones and comparing our solution to existing crowd-sensing platforms.

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Notes

  1. 1.

    https://en.wikipedia.org/wiki/Crowdsourcing.

  2. 2.

    http://researcher.watson.ibm.com/researcher/view_project.php?id=3011.

  3. 3.

    https://www.windowsazure.com.

  4. 4.

    https://www.kace.com.

  5. 5.

    http://aws.amazon.com/ec2.

  6. 6.

    http://www.cloudbees.com.

  7. 7.

    https://openshift.redhat.com.

  8. 8.

    https://www.dotcloud.com.

  9. 9.

    http://jelastic.com.

  10. 10.

    http://www.heroku.com.

  11. 11.

    http://www.appfog.com.

  12. 12.

    http://www.gartner.com/newsroom/id/2623415.

  13. 13.

    http://www.apisense.net.

  14. 14.

    http://www.cartoradio.fr.

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Acknowledgments

This work is partially funded by the ANR (French National Research Agency) ARPEGE SocEDA project and the EU FP7 PaaSage project.

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Authors and Affiliations

  1. University Lille 1 - Inria, Villeneuve d’Ascq, France

    Nicolas Haderer, Fawaz Paraiso, Christophe Ribeiro, Philippe Merle, Romain Rouvoy & Lionel Seinturier

Authors
  1. Nicolas Haderer
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  2. Fawaz Paraiso
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  3. Christophe Ribeiro
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  4. Philippe Merle
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  5. Romain Rouvoy
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  6. Lionel Seinturier
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Corresponding author

Correspondence to Lionel Seinturier .

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Editors and Affiliations

  1. State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, Beijing, China

    Wei Li

  2. Dept. of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina, USA

    Michael N. Huhns

  3. School of Computing, Informatics & Decision Systems Engineering, Arizona State University Dept. Computer Science & Engineering, Tempe, Arizona, USA

    Wei-Tek Tsai

  4. School of Computer Science & Engineering, Beihang University, Beijing, China

    Wenjun Wu

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Haderer, N., Paraiso, F., Ribeiro, C., Merle, P., Rouvoy, R., Seinturier, L. (2015). A Cloud-Based Infrastructure for Crowdsourcing Data from Mobile Devices. In: Li, W., Huhns, M., Tsai, WT., Wu, W. (eds) Crowdsourcing. Progress in IS. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47011-4_13

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