Advertisement

A Security System Based on Door Movement Detecting

  • Ci-Rong Li
  • Chie-Yang Kuan
  • Bing-Zhe He
  • Wu-En Wu
  • Chi-Yao Weng
  • Hung-Min Sun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 297)

Abstract

Recently, the smartphone devices have become the one of the most popular production in the whole world and the price of smartphone have become cheaper and cheaper. Especially, most of the smartphones have embedded lots of sensors such as light sensor, orientation sensor, accelerometer sensor, etc. Thus, our research is implemented a security application called DoorPass which based on smartphone device sensors. By placing the smartphone behind the door, DoorPass can detect the door movement, and provide some protection to the user. We provided three kinds of notification which are sending sms, make a phone call, and send email. Besides, we also provide three different functions for protection which are track phone, video record, and face detection. By implementing on the smartphone, DoorPass not only can provide the protection but also lower down the cost fee of buying the security hardware and provided convenient, simple and security functions.

Keywords

Security System Object Moving Detection Smart Phone App 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Ravindranath, L., Newport, C., Balakrishnan, H., Madden, S.: Improving wireless network performance using sensor hints. In: Proceedings of the 8th USENIX Conference on Networked Systems Design and Implementation, p. 21. USENIX Association (2011)Google Scholar
  5. 5.
  6. 6.
    Wang, E.K., Ye, Y., Xu, X.: Location-based distributed group key agreement scheme for vehicular ad hoc network. International Journal of Distributed Sensor Networks 2014 (2014)Google Scholar
  7. 7.
    Wei-Chi, K., Chien-Ming, C., Hui-Lung, L.: Cryptanalysis of a variant of peyravian-zunic’s password authentication scheme. IEICE Transactions on Communications 86(5), 1682–1684 (2003)Google Scholar
  8. 8.
    Chen, C.M., Chen, Y.H., Lin, Y.H., Sun, H.M.: Eliminating rouge femtocells based on distance bounding protocol and geographic information. Expert Systems with Applications 41(2), 426–433 (2014)CrossRefMathSciNetGoogle Scholar
  9. 9.
    He, B.Z., Chen, C.M., Su, Y.P., Sun, H.M.: A defence scheme against identity theft attack based on multiple social networks. Expert Systems with Applications 41(5), 2345–2352 (2014)CrossRefGoogle Scholar
  10. 10.
    Sun, H.M., Wang, H., Wang, K.H., Chen, C.M.: A native apis protection mechanism in the kernel mode against malicious code. IEEE Transactions on Computers 60(6), 813–823 (2011)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Wu, T.Y., Tseng, Y.M.: Further analysis of pairing-based traitor tracing schemes for broadcast encryption. Security and Communication Networks 6(1), 28–32 (2013)CrossRefGoogle Scholar
  12. 12.
    Chen, C.M., Wang, K.H., Wu, T.Y., Pan, J.S., Sun, H.M.: A scalable transitive human-verifiable authentication protocol for mobile devices. IEEE Transactions on Information Forensics and Security 8(8), 1318–1330 (2013)CrossRefGoogle Scholar
  13. 13.
    Chen, C.M., Lin, Y.H., Chen, Y.H., Sun, H.M.: Sashimi: secure aggregation via successively hierarchical inspecting of message integrity on wsn. Journal of Information Hiding and Multimedia Signal Processing 4(1), 57–72 (2013)CrossRefGoogle Scholar
  14. 14.
    He, Z.Y., Jin, L.W.: Activity recognition from acceleration data using ar model representation and svm. In: 2008 International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2245–2250. IEEE (2008)Google Scholar
  15. 15.
    He, Z., Jin, L.: Activity recognition from acceleration data based on discrete consine transform and svm. In: IEEE International Conference on Systems, Man and Cybernetics, SMC 2009, pp. 5041–5044. IEEE (2009)Google Scholar
  16. 16.
    Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Casale, P., Pujol, O., Radeva, P.: Human activity recognition from accelerometer data using a wearable device. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 289–296. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Ravi, N., Dandekar, N., Mysore, P., Littman, M.L.: Activity recognition from accelerometer data. In: AAAI, pp. 1541–1546 (2005)Google Scholar
  19. 19.
    Shin, S.H., Yeo, J.Y., Ji, S.H., Jeong, G.M.: An analysis of vibration sensors for smartphone applications using camera. In: 2011 International Conference on ICT Convergence (ICTC), pp. 772–773. IEEE (2011)Google Scholar
  20. 20.
    Xu, Z., Bai, K., Zhu, S.: Taplogger: Inferring user inputs on smartphone touchscreens using on-board motion sensors. In: Proceedings of the Fifth ACM Conference on Security and Privacy in Wireless and Mobile Networks, pp. 113–124. ACM (2012)Google Scholar
  21. 21.
    Cai, L., Chen, H.: Touchlogger: inferring keystrokes on touch screen from smartphone motion. In: Proceedings of the 6th USENIX Conference on Hot Topics in Security, p. 9. USENIX Association (2011)Google Scholar
  22. 22.
    Owusu, E., Han, J., Das, S., Perrig, A., Zhang, J.: Accessory: password inference using accelerometers on smartphones. In: Proceedings of the Twelfth Workshop on Mobile Computing Systems & Applications, p. 9. ACM (2012)Google Scholar
  23. 23.
    Cai, L., Chen, H.: On the practicality of motion based keystroke inference attack. In: Katzenbeisser, S., Weippl, E., Camp, L.J., Volkamer, M., Reiter, M., Zhang, X. (eds.) Trust 2012. LNCS, vol. 7344, pp. 273–290. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  24. 24.
    Han, J., Owusu, E., Nguyen, L.T., Perrig, A., Zhang, J.: Accomplice: Location inference using accelerometers on smartphones. In: 2012 Fourth International Conference on Communication Systems and Networks (COMSNETS), pp. 1–9. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ci-Rong Li
    • 1
  • Chie-Yang Kuan
    • 2
  • Bing-Zhe He
    • 2
  • Wu-En Wu
    • 3
  • Chi-Yao Weng
    • 2
  • Hung-Min Sun
    • 2
  1. 1.Department of ManagementFuqing Branch of Fujian Normal UniversityFujianChina
  2. 2.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan
  3. 3.Department of MathematicsSoochow UniversityTaipeiTaiwan

Personalised recommendations