Crowdsensing Based Citizen’s Safety Service

  • Zakaria BoucettaEmail author
  • Abdelaziz El Fazziki
  • Mohamed El adnani
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)


The widespread adoption of programmable mobile devices that have great sensing, collecting and analysing abilities, opened up multiple new paradigms such as crowdsensing. The addiction of people to their smartphones made it possible to these later to be a part of their daily life and activities, which lead to the creation of applications that require the combination of human participation and the use of the powerful new technologies embedded inside the mobile devices. These information systems help mainly in the gathering of historical and real-time data and in the analysing process. In this work, we present a framework dedicated to authorities that aims to motivate citizens to join a crowd sensing attempt to minimize and control the human and material damage that occurs when having deteriorated roads and non-responsible drivers.


Crowdsensing Data warehousing Information system Mobile devices 


  1. 1.
    Campbell, A.T., Eisenman, S.B., Lane, N.D., et al.: The rise of people-centric sensing. IEEE Internet Comput. 12(4), 12–21 (2008)CrossRefGoogle Scholar
  2. 2.
    Kamel Boulos, M.N., Resch, B., Crowley, D.N., et al.: Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int. J. Health Geogr. 10, 1–29 (2011)CrossRefGoogle Scholar
  3. 3.
    Boulif, M.N.: Maroc: moins de morts sur les routes en 2017 (2018)Google Scholar
  4. 4.
    Tribune: Maroc: 80% des accidents seraient dus au facteur humain (2017)Google Scholar
  5. 5.
    Bajwa, R., Rajagopal, R., Varaiya, P., Kavaler, R., Street, N.: In-pavement wireless sensor network for vehicle classification. In: Proceedings of the 10th ACM/IEEE International Conference on Information Processing Sensor Networks, Aug 2016, pp. 85–96 (2011)Google Scholar
  6. 6.
    Annan, A.P.: Ground penetrating radar: principles electromagnetic principles of ground penetrating radarGoogle Scholar
  7. 7.
    Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., Zeinalipour-yazti, D.: Crowdsourcing with smartphones, pp. 1–7 (2012)CrossRefGoogle Scholar
  8. 8.
    Zhang, D., Wang, L., Xiong, H., Guo, B.: 4W1H in mobile crowd sensing. IEEE Commun. Mag. 52(8), 42–48 (2014)CrossRefGoogle Scholar
  9. 9.
    STREET BUMP [Internet] (2017). Available from:
  10. 10.
    Brisimi, T.S., Cassandras, C.G., Osgood, C., Paschalidis, I.C., Zhang, Y.: Sensing and classifying roadway obstacles in smart cities: the street bump system. IEEE Access 4(c), 1301–1312 (2016)Google Scholar
  11. 11.
    Kalim, F., Jeong, J., Ilyas, M.U.: CRATER: a crowd sensing application to estimate road conditions. IEEE Access 4, 8317–8326 (2016)CrossRefGoogle Scholar
  12. 12.
    Chen, K., Tan, G., Lu, M., Wu, J.: CRSM: a practical crowdsourcing-based road surface monitoring system. Wirel. Netw. 22(3), 765–779 (2016)CrossRefGoogle Scholar
  13. 13.
    Xue, G., Zhu, H., Hu, Z., Yu, J., Zhu, Y., Luo, Y.: Pothole in the dark: perceiving pothole profiles with participatory urban vehicles. IEEE Trans. Mob. Comput. 16(5), 1408–1419 (2017)CrossRefGoogle Scholar
  14. 14.
    Li, Z., Kolmanovsky, I.V., Kalabic, U.V., Atkins, E.M., Lu, J.: Filev DiP. Optimal state estimation for systems driven by jump-diffusion process with application to road anomaly detection. IEEE Trans. Control Syst. Technol. 25(5), 1634–1643 (2017)Google Scholar
  15. 15.
    Fox, A., Kumar, B.V.K.V., Chen, J., Bai, F.: Multi-lane pothole detection from crowdsourced undersampled vehicle sensor data. IEEE Trans. Mob. Comput. 16(12), 3417–3430 (2017)CrossRefGoogle Scholar
  16. 16.
    Dang, V.C., Kubo, M., Sato, H., Yamaguchi, A., Namatame, A.: A simple braking model for detecting incidents locations by smartphones. In: Proceedings of the 2014 7th IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2014 (2015)Google Scholar
  17. 17.
    Dai, J., Teng, J., Bai, X.: Mobile phone based drunk driving detection. In: 2010 4th International … [Internet], pp. 1–8 (2010). Available from:
  18. 18.
    Eren, H., Makinist, S., Akin, E., Yilmaz, A.: Estimating driving behavior by a smartphone. In: IEEE Intelligent Vehicles Symposium, Proceedings, pp. 234–239 (2012)Google Scholar
  19. 19.
    Johnson, D.A., Trivedi, M.M.: Driving style recognition using a smartphone as a sensor platform. In: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, pp. 1609–1615 (2011)Google Scholar
  20. 20.
    Bhoraskar, R., Vankadhara, N., Raman, B., Kulkarni, P.: Wolverine: traffic and road condition estimation using smartphone sensors. In: 2012 4th International Conference on Communication Systems and Networks, COMSNETS 2012 (2012)Google Scholar
  21. 21.
    Saiprasert, C., Pholprasit, T., Pattara-Atikom, W.: Detecting driving events using smartphone. In: 20th ITS World Congress [Internet], 1–12 Oct 2013. Available from:,
  22. 22.
    Fazeen, M., Gozick, B., Dantu, R., Bhukhiya, M., González, M.C.: Safe driving using mobile phones. IEEE Trans. Intell. Trans. Syst. Internet. 13(3), 1462–1468 (2012). Available from: Scholar
  23. 23.
    White, J., Thompson, C., Turner, H., Dougherty, B., Schmidt, D.C.: WreckWatch: automatic traffic accident detection and notification with smartphones. Mob. Netw. Appl. 16(3), 285–303 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zakaria Boucetta
    • 1
    Email author
  • Abdelaziz El Fazziki
    • 1
  • Mohamed El adnani
    • 1
  1. 1.Computer Systems Engineering Laboratory (LISI), Faculty of Sciences-UCAMMarrakeshMorocco

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