Sensor Data Management for Driver Monitoring System

  • Chee Een Yap
  • Myung Ho KimEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9502)


Road accident becomes a threat to all drivers around the world. According to the study, fatigue or drowsiness is one of the causes to road accident. As the rapid development of the mobile devices and sensor networks, mobile based driver monitoring system has been widely proposed and discussed as an effort to reduce road accident rate around the world. Sensors such as EEG, temperature or respiration sensor are used to collect the signal from the driver to alarm the driver if drowsiness is likely to happen. However, the sensor data management of the collected data(signals) is not being paid enough attention. In this paper, we propose a sensor data management mechanism for the mobile based driver monitoring system to handle the data in a more efficient manner.


Sensor data management Driver monitoring Invertible bloom filter 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF-2014R1A1A2058695).


  1. 1.
    Akyildiz, I.F., et al.: A survey on sensor networks. Commun. Mag. IEEE 40(8), 102–114 (2002)CrossRefGoogle Scholar
  2. 2.
    Lorincz, K., et al.: Sensor networks for emergency response: challenges and opportunities. IEEE Pervasive Comput. 3(4), 16–23 (2004)CrossRefGoogle Scholar
  3. 3.
    Zhang, R., et al.: Logistics transportation vehicle system for information acquisition based on wireless sensor network. Procedia Eng. 29, 3954–3958 (2012)CrossRefGoogle Scholar
  4. 4.
    Basu, D., et al. : Wireless sensor network based smart home: sensor selection, deployment and monitoring. In: Sensors Applications Symposium (SAS). IEEE (2013)Google Scholar
  5. 5.
    Alemdar, H., Ersoy, C.: Wireless sensor networks for healthcare: a survey. Comput. Netw. 54(15), 2688–2710 (2010)CrossRefGoogle Scholar
  6. 6.
    Mainwaring, A., et al.: Wireless sensor networks for habitat monitoring. In: Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, pp. 88–97. ACM, Atlanta, Georgia, USA (2002)Google Scholar
  7. 7.
    Maloberti, F., Malcovati, P.: Microsystems and smart sensor interfaces: a review. Analog Integr. Circ. Sig. Process. 15(1), 9–26 (1998)CrossRefGoogle Scholar
  8. 8.
    IBM: What is big data? (2012).
  9. 9.
    Laney, D.: The Importance of Big Data: A Definition (2012)Google Scholar
  10. 10.
    Balazinska, M., et al.: Data management in the worldwide sensor web. Pervasive Comput. IEEE 6(2), 30–40 (2007)CrossRefGoogle Scholar
  11. 11.
    Organization, W.H.: Global status report on road safety 2013 (2013)Google Scholar
  12. 12.
    Sigari, M.-H., Fathy, M., Soryani, M.: A driver face monitoring system for fatigue and distraction detection. Int. J. Veh. Technol., pp. 11 (2013)Google Scholar
  13. 13.
    Rogado, E., et al.: Driver fatigue detection system. In: IEEE International Conference on Robotics and Biomimetics, ROBIO 2008 (2008)Google Scholar
  14. 14.
    Wen-Chang, C., et al.: A fatigue detection system with eyeglasses removal.In: 15th International Conference on Advanced Communication Technology, ICACT 2013 (2013)Google Scholar
  15. 15.
    Horn, W.-B., Chen, C.-Y.: A real-time driver fatigue detection system based on eye tracking and dynamic template matching. Tamkang J. Sci. Eng. 11(1), 65–72 (2008)Google Scholar
  16. 16.
    Jin, Z., D. Jun, and Y. Honglue.: Driving Status’ Monitoring and Alarming System Based on Information Fusion Technology. in Intelligent Control and Automation, WCICA, The Sixth World Congress on. 2006 (2006)Google Scholar
  17. 17.
    Aadi, M.F.K.a.F.: Efficient Car Alarming System for Fatigue Detection during Driving. International Journal of Innovation, Management and Technology, 3(4), 6 pages (2012)Google Scholar
  18. 18.
    Lee, B.-G., Lee, B.-L., Chung, W.-Y.: Mobile healthcare for automatic driving sleep-onset detection using wavelet-based EEG and respiration signals. Sensors 14(10), 17915–17936 (2014)CrossRefGoogle Scholar
  19. 19.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)CrossRefzbMATHGoogle Scholar
  20. 20.
    Osano, T., Y. Uchida, and N. Ishikawa.: Routing Protocol Using Bloom Filters for Mobile Ad Hoc Networks. in Mobile Ad-hoc and Sensor Networks, MSN 2008. The 4th International Conference on. 2008. (2008)Google Scholar
  21. 21.
    Mitzenmacher, A.B.a.M.M.a.A.B.I.M.: Network Applications of Bloom Filters: A Survey. Internet Mathematics, 10 pages (2002)Google Scholar
  22. 22.
    Ross, M.C.a.C.A.L.a.G.A.M.a.K.A.: Buffered Bloom filters on solid state storage. in In First Intl. Workshop on Accelerating Data Management Systems Using Modern Processor and Storage Architectures (ADMS*10). (2010)Google Scholar
  23. 23.
    Li, W., et al.: Accurate Counting Bloom Filters for Large-Scale Data Processing. Mathematical Problems in Engineering, 2013, 11 pages (2013)Google Scholar
  24. 24.
    Yongsheng Hao, Z.G.: Redundancy Removal Approach for Integrated RFID Readers with Counting Bloom Filter. Journal of Computational Information Systems, 9(5),8 pages(2013)Google Scholar
  25. 25.
    Eppstein, D. and M.T. Goodrich.: Straggler Identification in Round-Trip Data Streams via Newton’s Identities and Invertible Bloom Filters IEEE Trans. on Knowl. and Data Eng., 23(2)297–306 (2011)Google Scholar
  26. 26.
    Fan, L., et al.: Summary cache: a scalable wide-area web cache sharing protocol. IEEE/ACM Trans. Netw. 8(3), 281–293 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of ComputingSoongsil UniversitySeoulKorea
  2. 2.School of SoftwareSoongsil UniversitySeoulKorea

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