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iKey: An Intelligent Key System Based on Efficient Inclination Angle Sensing Techniques

  • Conference paper
  • First Online:
Wireless Algorithms, Systems, and Applications (WASA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10874))

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Abstract

The elderly may have different aspects of inconvenience in their daily life. Among them, many old people have trouble remembering things even just happened hours ago. They often forget whether they have locked the door while leaving so that they may have to return and check. Such situation also happens to many younger people that do not concentrate their mind while locking the door. In this paper, an intelligent key system, iKey, is proposed to solve such problem. It can be deployed on an existing key to detect user’s locking actions and store locking status in the form of time. Related hardware architecture and working process are proposed. The sensing module based on inclination angle sensors is designed to reduce the amount of data generated. Furthermore, efficient locking detection algorithms are proposed accordingly. Such system and techniques can also be applied in knobs or rotating handles of machines and facilities to detect illegal operations and to avoid user’s forgetting to operate them.

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References

  1. Lutz, W., Sanderson, W., Scherbov, S.: The coming acceleration of global population aging. Nature 451(7179), 716–719 (2008)

    Article  Google Scholar 

  2. Cai, Z., Zheng, X.: A private and efficient mechanism for data uploading in smart cyber-physical systems. Trans. Netw. Sci. Eng. (TNSE) (2018)

    Google Scholar 

  3. Liang, Y., Cai, Z., Yu, J., Han, Q., Li, Y.: Deep learning based inference of private information using embedded sensors in smart devices. IEEE Netw. Mag. (2018)

    Google Scholar 

  4. Zhang, L., Cai, Z., Wang, X.: FakeMask: a novel privacy preserving approach for smartphones. IEEE Trans. Netw. Serv. Manag. 13(2), 335–348 (2016)

    Article  Google Scholar 

  5. Zheng, X., Cai, Z., Li, Y.: Data linkage in smart IoT systems: a consideration from privacy perspective. IEEE Commun. Mag. (2018)

    Google Scholar 

  6. Sanchez, I., Satta, R., Fovino, I.N., Baldini, G., Steri, G., Shaw, D., Ciardulli, A.: Privacy leakages in smart home wireless technologies. In: Proceedings of International Carnahan Conference on Security Technology, pp. 1–6. IEEE (2014)

    Google Scholar 

  7. Duan, Z., Li, W., Cai, Z.: Distributed auctions for task assignment and scheduling in mobile crowdsensing systems. In: Proceedings of International Conference on Distributed Computing Systems, pp. 635–644. IEEE (2017)

    Google Scholar 

  8. Liu, J., Zhong, L., Wickramasuriya, J., Vasudevan, V.: uWave: accelerometer-based personalized gesture recognition and its applications. Pervasive Mob. Comput. 5(6), 657–675 (2009)

    Article  Google Scholar 

  9. Zhang, X., Chen, X., Li, Y., Lantz, V., Wang, K., Yang, J.: A framework for hand gesture recognition based on accelerometer and EMG sensors. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 41(6), 1064–1076 (2011)

    Article  Google Scholar 

  10. Lin, K., Cheng, S., Li, Y., Li, J., Gao, H., Wang, H.: SHMDRS: a smartphone-based human motion detection and response system. In: Yang, Q., Yu, W., Challal, Y. (eds.) WASA 2016. LNCS, vol. 9798, pp. 174–185. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42836-9_16

    Chapter  Google Scholar 

  11. Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for data mining applications. In: Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 285–289. ACM (2000)

    Google Scholar 

  12. Zhang, S., Li, X., Zong, M., Zhu, X., Wang, R.: Efficient kNN classification with different numbers of nearest neighbors. IEEE Trans. Neural Netw. Learn. Syst. 29(5), 1774–1785 (2017)

    Article  Google Scholar 

  13. Song, G., Rochas, J., Beze, L., Huet, F., Magoules, F.: K nearest neighbour joins for big data on mapreduce: a theoretical and experimental analysis. IEEE Trans. Knowl. Data Eng. 28(9), 2376–2392 (2016)

    Article  Google Scholar 

  14. Maxim Integrated. https://para.maximintegrated.com/en/results.mvp?fam=rtc&tree=master

  15. Powers, D.M.W.: Applications and explanations of Zipf’s law. In: Advances in Neural Information Processing Systems, vol. 5, no. 4, pp. 595–599 (1998)

    Google Scholar 

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Acknowledgments

This work is supported in part by the National Natural Science Foundation of China under Grant No. 61632010, 61502116, 61370217, and U1509216.

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Correspondence to Jinbao Wang .

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Lin, K., Wang, J., Li, J., Cheng, S., Gao, H. (2018). iKey: An Intelligent Key System Based on Efficient Inclination Angle Sensing Techniques. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-94268-1_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94267-4

  • Online ISBN: 978-3-319-94268-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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