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Crowdsourcing pp 243-265 | Cite as

A Cloud-Based Infrastructure for Crowdsourcing Data from Mobile Devices

  • Nicolas Haderer
  • Fawaz Paraiso
  • Christophe Ribeiro
  • Philippe Merle
  • Romain Rouvoy
  • Lionel SeinturierEmail author
Chapter
Part of the Progress in IS book series (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.

Keywords

Mobile Phone Mobile Device Mobile Node Cloud Provider Cloud Environment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

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

References

  1. 1.
    Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A.: Social fMRI: investigating and shaping social mechanisms in the real world. Pervasive Mob. Comput. 7(6), 643–659 (2011)CrossRefGoogle Scholar
  2. 2.
    Biagioni, J., Gerlich, T., Merrifield, T., Eriksson, J.: EasyTracker: automatic transit tracking, mapping, and arrival time prediction using smartphones. In: 9th International Conference on Embedded Networked Sensor Systems, pp. 68–81. ACM (2011). doi: 10.1145/2070942.2070950
  3. 3.
    Brouwers, N., Woehrle, M., Stern, R., Kalech, M., Feldman, A., Provan, G., Malazi, H., Zamanifar, K., Khalili, A., Dulman, S., et al.: Pogo, a middleware for mobile phone sensing. In: 13th International Middleware Conference, pp. 106–113. Springer (2012)Google Scholar
  4. 4.
    Bruneton, E., Coupaye, T., Leclercq, M., Quéma, V., Stefani, J.B.: The fractal component model and its support in Java: experiences with auto-adaptive and reconfigurable systems. Softw.: Pract. Exp. (SPE) 36(11–12), 1257–1284 (2006)Google Scholar
  5. 5.
    Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., Srivastava, M.B.: Participatory sensing. In: Workshop on World-Sensor-Web (WSW’06): Mobile Device Centric Sensor Networks and Applications, pp. 117–134 (2006)Google Scholar
  6. 6.
    Choi, H., Chakraborty, S., Greenblatt, M., Charbiwala, Z., Srivastava, M.: SensorSafe: managing health-related sensory information with fine-grained privacy controls. Technical report, TR-UCLA-NESL-201009-01 (2010)Google Scholar
  7. 7.
    Cuff, D., Hansen, M., Kang, J.: Urban sensing: out of the woods. Commun. ACM 51(3), 24–33 (2008)CrossRefGoogle Scholar
  8. 8.
    Das, T., Mohan, P., Padmanabhan, V., Ramjee, R., Sharma, A.: Prism: platform for remote sensing using smartphones. In: 8th International Conference on Mobile Systems, Applications, and Services, pp. 63–76. ACM (2010)Google Scholar
  9. 9.
    Erl, T.: SOA: Principles of Service Design, vol. 1. Prentice Hall, Upper Saddle River (2008)Google Scholar
  10. 10.
    Falaki, H., Mahajan, R., Estrin, D.: SystemSens: a tool for monitoring usage in smartphone research deployments. In: 6th International Workshop on MobiArch, pp. 25–30. ACM (2011)Google Scholar
  11. 11.
    Froehlich, J., Chen, M., Consolvo, S., Harrison, B., Landay, J.: MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones. In: 5th International Conference on Mobile Systems, Applications, and Services, pp. 57–70. ACM (2007)Google Scholar
  12. 12.
    Kephart, J.: An architectural blueprint for autonomic computing. IBM White paper (2006)Google Scholar
  13. 13.
    Killijian, M.O., Roy, M., Trédan, G.: Beyond Francisco cabs: building a *-lity mining dataset. In: Workshop on the Analysis of Mobile Phone Networks (2010)Google Scholar
  14. 14.
    Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.: A survey of mobile phone sensing. IEEE Commun. Mag. 48(9), 140–150 (2010)CrossRefGoogle Scholar
  15. 15.
    Liu, P., Chen, Y., Tang, W., Yue, Q.: Mobile WEKA as data mining tool on android. Adv. Electr. Eng. Autom. 139, 75–80 (2012)Google Scholar
  16. 16.
    Mell, P., Grance, T.: The NIST Definition of Cloud Computing. Technical report, National Institute of Standards and Technology. http://www.nist.gov/itl/cloud/upload/cloud-def-v15.pdf (2009)
  17. 17.
    Miluzzo, E., Lane, N., Lu, H., Campbell, A.: Research in the App store era: experiences from the CenceMe App deployment on the iPhone. In: 1st International Work. Research in the Large: Using App Stores, Markets, and Other Wide Distribution Channels in UbiComp Research (2010)Google Scholar
  18. 18.
    Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West, R., Boda, P.: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: 7th International Conference on Mobile Systems, Applications, and Services, pp. 55–68. ACM (2009)Google Scholar
  19. 19.
    OASIS: Reference Model for Service Oriented Architecture 1.0. http://oasis-open.org/committees/download.php/19679/soa-rm-cs.pdf (2006)
  20. 20.
    Paraiso, F., Haderer, N., Merle, P., Rouvoy, R., Seinturier, L.: A federated multi-cloud PaaS infrastructure. In: 5th IEEE International Conference on Cloud Computing, pp. 392–399. United States (2012). doi: 10.1109/CLOUD.2012.79
  21. 21.
    Paraiso, F., Merle, P., Seinturier, L.: Managing elasticity across multiple cloud providers. In: 1st International Workshop on Multi-Cloud Applications and Federated Clouds. Prague, Czech, Republic (2013). http://hal.inria.fr/hal-00790455
  22. 22.
    Seinturier, L., Merle, P., Rouvoy, R., Romero, D., Schiavoni, V., Stefani, J.B.: A component-based middleware platform for reconfigurable service-oriented architectures. Softw.: Pract. Exp. (SPE) 42(5), 559–583 (2012)Google Scholar
  23. 23.
    Shepard, C., Rahmati, A., Tossell, C., Zhong, L., Kortum, P.: LiveLab: measuring wireless networks and smartphone users in the field. ACM SIGMETRICS Perform. Eval. Rev. 38(3), 15–20 (2011)CrossRefGoogle Scholar
  24. 24.
    Shin, M., Cornelius, C., Peebles, D., Kapadia, A., Kotz, D., Triandopoulos, N.: AnonySense: a system for anonymous opportunistic sensing. Pervasive Mob. Comput. 7(1), 16–30 (2011)CrossRefGoogle Scholar
  25. 25.
    Szyperski, C.: Component Software: Beyond Object-Oriented Programming. ACM Press and Addison-Wesley, New York (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Nicolas Haderer
    • 1
  • Fawaz Paraiso
    • 1
  • Christophe Ribeiro
    • 1
  • Philippe Merle
    • 1
  • Romain Rouvoy
    • 1
  • Lionel Seinturier
    • 1
    Email author
  1. 1.University Lille 1 - InriaVilleneuve d’AscqFrance

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