Advertisement

User Participatory Sensing for Disaster Detection and Mitigation in Urban Environments

  • Shin’ichi Konomi
  • Kazuki Wakasa
  • Masaki Ito
  • Kaoru Sezaki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9749)

Abstract

Pervasive communication technologies have opened up the opportunities for citizens to cope with disasters by exploiting networked mobile devices. However, existing approaches often overlook the brittleness of the technological infrastructures and rely heavily on users’ manual inputs. In this paper, we propose a robust and resilient sensing environment by extending and integrating cooperative location inference and participatory sensing using smartphones and IoTs. The proposed approach encourages proactive engagement in disaster mitigation by means of everyday data collection and end-user deployment of IoT sensors.

Keywords

Participatory sensing Disaster mitigation Smartphones IoT Urban environments Civic computing 

Notes

Acknowledgments

We acknowledge Prof. Toshihiro Osaragi for providing us the mobility simulation data right after a great earthquake. This work was supported by CREST, JST.

References

  1. 1.
    Inoguchi, M., Tamura, K., Sudo, S., Hayashi, H.: Implementation of prototype mobile application operated on smartphones for micromedia service. J. Disaster Res. 9(2), 139–148 (2014)CrossRefGoogle Scholar
  2. 2.
    Gao, H., Barbier, G., Goolsby, R.: Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intell. Syst. 26, 10–14 (2011)CrossRefGoogle Scholar
  3. 3.
    Olteanu, A., Vieweg, S., Castillo, C., What to expect when the unexpected happens: social media communications across crises. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW 2015), pp. 994–1009 (2015)Google Scholar
  4. 4.
    Goodchild, M.F., Glennon, J.A.: Crowdsourcing geographic information for disaster response: a research frontier. Int. J. Digital Earth 3(3), 231–241 (2010)CrossRefGoogle Scholar
  5. 5.
    Disaster Reporter. http://www.fema.gov/disaster-reporter. Accessed 1 Jan 2015
  6. 6.
    Naito, S., Azuma, H., Senna, S., Yoshizawa, M., Nakamura, H., Hao, K., Fujiwara, H., Hirayama, Y., Yuki, N., Yoshida, M.: Development and testing of a mobile application for recording and analyzing seismic data. J. Disaster Res. 8(5), 990–1000 (2013)CrossRefGoogle Scholar
  7. 7.
    Faulkner, M., Clayton, R., Heaton, T., Chandy, K.M., Kohler, M., Bunn, J., Guy, R., Liu, A., Olson, M., Cheng, M., Krause, A.: Community sense, response systems: your phone as quake detector. CACM 57(7), 66–75 (2014)CrossRefGoogle Scholar
  8. 8.
    EMSC, Citizen Seismology. http://www.citizenseismology.eu/. Accessed 1 Jan 2015
  9. 9.
    Meyer, P.: Using flash crowds to automatically detect earthquakes and impact before anyone else. http://irevolution.net/2014/10/27/using-flashsourcing-to-automatically-detect-earthquakes/. Accessed 1 Jan 2015
  10. 10.
    Amjad, M.M.M.: Naive bayes classifier-based fire detection using smartphone sensors, Master’s Thesis, University of AgderGoogle Scholar
  11. 11.
    Gartner, Inc, Says, Gartner, 4.9 Billion Connected “Things” Will Be in Use in (2015). http://www.gartner.com/newsroom/id/2905717. Accessed 1 Oct 2015
  12. 12.
    Shimizu, K., Iwai, M., Sezaki, K.: Social link analysis using wireless beaconing and accelerometer. In: IEEE 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 33–38 (2013)Google Scholar
  13. 13.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.: Activity recognition from accelerometer data. In: Proceedings of the National Conference on Artificial Intelligence, vol. 20, no. 3, p. 1541 (2005)Google Scholar
  14. 14.
    Dang, C., Iwai, M., Umeda, K., Tobe, Y., Sezaki, K.: NaviComf: navigate pedestrians for comfort using multi-modal environmental sensors. In: IEEE Pervasive Computing and Communication (Percom 2012), Switzerland, March 2012Google Scholar
  15. 15.
    Esponda, E., Guerrero, V.M.: Surveys with negative questions for senstive items. Statics Probab. Lett. 79(24), 2456–2461 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Konomi, S., Kostakos, V., Sezaki, K., Shibasaki, R.: Crowd sensing for disaster response and preparedness. In: The 77th National Concention of IPSJ, pp. 449–451 (2015)Google Scholar
  17. 17.
    Huang, L., Matsuura, K., Sezaki, K.: Enhancing wiereless location privacy using silent period. WCNC 2005, 1187–1192 (2005)Google Scholar
  18. 18.
    Matsuno, Y., Ito, M., Sezaki, K.: Impact of time-varying population density on location privacy preservation level. In: The 5th IEEE International Workshop on the Impact of Human Mobility in Pervasive Systems and Applications (IEEE PerMoby). Sydney, Australia (2016)Google Scholar
  19. 19.
    Umeda, K., Hashimoto, Y., Nakanishi, T., Irie, K., Terabayashi, K.: Subtraction stereo: a stereo camera system that focuses on moving regions. In: Proceedings of SPIE 7239, Three-Dimensional Imaging Metrology, p. 723908 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Shin’ichi Konomi
    • 1
  • Kazuki Wakasa
    • 2
  • Masaki Ito
    • 3
  • Kaoru Sezaki
    • 1
    • 3
  1. 1.Center for Spatial Information ScienceThe University of TokyoKashiwaJapan
  2. 2.Department of Socio-Cultural Environmental StudiesThe University of TokyoKashiwaJapan
  3. 3.Institute of Industrial ScienceThe University of TokyoTokyoJapan

Personalised recommendations