Advertisement

NoizCrowd: A Crowd-Based Data Gathering and Management System for Noise Level Data

  • Mariusz Wisniewski
  • Gianluca Demartini
  • Apostolos Malatras
  • Philippe Cudré-Mauroux
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8093)

Abstract

Many systems require access to very large amounts of data to properly function, like systems allowing to visualize or predict meteorological changes in a country over a given period of time, or any other system holding, processing and displaying scientific or sensor data. However, filling out a database with large amounts of valuable data can be a difficult, costly and time-consuming task. In this paper, we present techniques to create large amounts of data by combining crowdsourcing, data generation models, mobile computing, and big data analytics. We have implemented our methods in a system, NoizCrowd, allowing to crowdsource noise levels in a given region and to generate noise models by using state-of-the-art noise propagation models and array data management techniques. The resulting models and data can then be accessed using a visual interface.

Keywords

Global Position System Noise Level Mobile Phone Sound Level Interpolation Model 
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.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barreiro-Hurl, J., Sanchez, M., Viladrich-Grau, M.: How much are people willing to pay for silence? a contingent valuation study. Applied Economics 37(11), 1233–1246 (2005)CrossRefGoogle Scholar
  2. 2.
    Burke, J., Estrin, D., Hansen, M., Parker, A., Ramanathan, N., Reddy, S., Srivastava, M.B.: Participatory sensing. In: Workshop on World-Sensor-Web (WSW 2006): Mobile Device Centric Sensor Networks and Applications, pp. 117–134 (2006)Google Scholar
  3. 3.
    Campbell, A.T., Eisenman, S.B., Lane, N.D., Miluzzo, E., Peterson, R.A., Lu, H., Zheng, X., Musolesi, M., Fodor, K., Ahn, G.-S.: The rise of people-centric sensing. IEEE Internet Computing 12(4), 12–21 (2008)CrossRefGoogle Scholar
  4. 4.
    Christin, D., Reinhardt, A., Kanhere, S.S., Hollick, M.: A survey on privacy in mobile participatory sensing applications. Journal of Systems and Software 84(11), 1928–1946 (2011)CrossRefGoogle Scholar
  5. 5.
    Cudré-Mauroux, P., Kimura, H., Lim, K.-T., Rogers, J., Simakov, R., Soroush, E., Velikhov, P., Wang, D.L., Balazinska, M., Becla, J., DeWitt, D.J., Heath, B., Maier, D., Madden, S., Patel, J.M., Stonebraker, M., Zdonik, S.B.: A Demonstration of SciDB: A Science-Oriented DBMS. PVLDB 2(2), 1534–1537 (2009)Google Scholar
  6. 6.
    Cuff, D., Hansen, M., Kang, J.: Urban sensing: out of the woods. ACM Communications 51(3), 24–33 (2008)CrossRefGoogle Scholar
  7. 7.
    Deng, L., Cox, L.P.: Livecompare: grocery bargain hunting through participatory sensing. In: Proceedings of the 10th Workshop on Mobile Computing Systems and Applications, HotMobile 2009, pp. 4:1–4:6. ACM, New York (2009)Google Scholar
  8. 8.
    DiSalvo, C., Nourbakhsh, I., Holstius, D., Akin, A., Louw, M.: The neighborhood networks project: a case study of critical engagement and creative expression through participatory design. In: Proceedings of the Tenth Anniversary Conference on Participatory Design 2008, PDC 2008, pp. 41–50. Indiana University, Indianapolis (2008)Google Scholar
  9. 9.
    DHondt, E., Stevens, M., Jacobs, A.: Participatory noise mapping works! an evaluation of participatory sensing as an alternative to standard techniques for environmental monitoring. In: Pervasive and Mobile Computing (2012)Google Scholar
  10. 10.
    Ganti, R.K., Pham, N., Ahmadi, H., Nangia, S., Abdelzaher, T.F.: Greengps: a participatory sensing fuel-efficient maps application. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, MobiSys 2010, pp. 151–164. ACM, New York (2010)Google Scholar
  11. 11.
    Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Communications Magazine 49(11), 32–39 (2011)CrossRefGoogle Scholar
  12. 12.
    Kanjo, E.: Noisespy: A real-time mobile phone platform for urban noise monitoring and mapping. Mob. Netw. Appl. 15(4), 562–574 (2010)CrossRefGoogle Scholar
  13. 13.
    Lamancusa, J.S.: Outdoor sound propagation, pp. 10.6–10.7. Penn State University, PAGoogle Scholar
  14. 14.
    Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Communications Magazine 48(9), 140–150 (2010)CrossRefGoogle Scholar
  15. 15.
    Lu, H., Pan, W., Lane, N.D., Choudhury, T., Campbell, A.T.: Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, MobiSys 2009, pp. 165–178. ACM, New York (2009)Google Scholar
  16. 16.
    Martí, I.G., Rodríguez, L.E., Benedito, M., Trilles, S., Beltrán, A., Díaz, L., Huerta, J.: Mobile application for noise pollution monitoring through gamification techniques. In: Herrlich, M., Malaka, R., Masuch, M. (eds.) ICEC 2012. LNCS, vol. 7522, pp. 562–571. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    Mendez, D., Labrador, M., Ramachandran, K.: Data interpolation for participatory sensing systems. Pervasive and Mobile Computing 9(1), 132–148 (2013); Special Section: Pervasive SustainabilityGoogle Scholar
  18. 18.
    Moudon, A.V.: Real noise from the urban environment: How ambient community noise affects health and what can be done about it. American Journal of Preventive Medicine 37(2), 167–171 (2009)CrossRefGoogle Scholar
  19. 19.
    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: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, MobiSys 2009, pp. 55–68. ACM, New York (2009)Google Scholar
  20. 20.
    Philipp, D., Stachowiak, J., Alt, P., Dürr, F., Rothermel, K.: DrOPS: Model-Driven Optimization for Public Sensing Systems. In: 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom) (PerCom 2013), pp. 1–8. IEEE Computer Society, San Diego (2013)Google Scholar
  21. 21.
    Rana, R.K., Chou, C.T., Kanhere, S.S., Bulusu, N., Hu, W.: Ear-phone: an end-to-end participatory urban noise mapping system. In: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010, pp. 105–116. ACM, New York (2010)CrossRefGoogle Scholar
  22. 22.
    Reddy, S., Estrin, D., Srivastava, M.: Recruitment framework for participatory sensing data collections. In: Floréen, P., Krüger, A., Spasojevic, M. (eds.) Pervasive 2010. LNCS, vol. 6030, pp. 138–155. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    Ruge, L., Altakrouri, B., Schrader, A.: Soundofthecity - continuous noise monitoring for a healthy city. In: 5th International Workshop on Smart Environments and Ambient Intelligence (SENAmI 2013) at IEEE International Conference on Pervasive Computing and Communication (PerCom 2013), San Diego, California, USA, March 18-22 (2013)Google Scholar
  24. 24.
    Schweizer, I., Bärtl, R., Schulz, A., Probst, F., Mühlhäuser, M.: Noisemap - real-time participatory noise maps. In: Second International Workshop on Sensing Applications on Mobile Phones, ACM SenSys 2011 (2011)Google Scholar
  25. 25.
    Seering, A., Cudré-Mauroux, P., Madden, S., Stonebraker, M.: Efficient Versioning for Scientific Array Databases. In: ICDE, pp. 1013–1024. IEEE Computer Society (2012)Google Scholar
  26. 26.
    Wylot, M., Pont, J., Wisniewski, M., Cudré-Mauroux, P.: dipLODocus [RDF]—Short and Long-Tail RDF Analytics for Massive Webs of Data. In: Aroyo, L., Welty, C., Alani, H., Taylor, J., Bernstein, A., Kagal, L., Noy, N., Blomqvist, E. (eds.) ISWC 2011, Part I. LNCS, vol. 7031, pp. 778–793. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mariusz Wisniewski
    • 1
  • Gianluca Demartini
    • 1
  • Apostolos Malatras
    • 2
  • Philippe Cudré-Mauroux
    • 1
  1. 1.eXascale InfolabUniversity of FribourgSwitzerland
  2. 2.Pervasive and Artificial Intelligence GroupUniversity of FribourgSwitzerland

Personalised recommendations

Cite paper

Buy options