Advertisement

Mobile Networks and Applications

, Volume 23, Issue 4, pp 709–716 | Cite as

The Individual Identification Method of Wireless Device Based on A Robust Dimensionality Reduction Model of Hybrid Feature Information

  • Hui Han
  • Jingchao Li
  • Xiang Chen
Article
  • 68 Downloads

Abstract

With the advent of Internet of things, the number of mobile, and embedded, wearable devices are on the rising nowadays, which make us increasingly faced with the limitations of traditional network security control. Hence, accurately identifying different wireless devices through hybrid information processing method for the Internet of things becomes very important today. To this problem, we design, implement, and evaluate a robust algorithm to identify the wireless device with fingerprint features through integral envelope and Hilbert transform theory based PCA analysis algorithm. Integral envelope theory and Hilbert transform theory was used respectively to process the signals first, then the principal component features can be extracted by PCA analysis algorithm. At last, gray relation classifier was used to identify the signals. We experimentally demonstrate the effectiveness of the proposed algorithm to differentiat between 10 numbers of wireless device with the accuracy in excess of 99%. The approach itself is general and will work with any wireless devices’ recognition.

Keywords

Internet of things Individual recognition PCA analysis Integral envelope theory Hilbert transform theory Hybrid information processing 

Notes

Acknowledgments

This research is supported by the National Natural Science Foundation of China (No. 61603239) and (No. 61601281).

References

  1. 1.
    Liu S, Lu M, Liu G, Zheng P (2017) A Novel Distance Metric: Generalized Relative Entropy. Entropy 19(6):269CrossRefGoogle Scholar
  2. 2.
    Liu S, Zhang Z, Qi L, Ma M (2016) Multimedia Tools and Applications, 75, (23), 15525–15536Google Scholar
  3. 3.
    Lin Y, Wang C, Chunguang M, Zheng D, Xuefei M (2016) A new combination method for multisensor conflict information[J]. J Supercomput 1:1–17Google Scholar
  4. 4.
    Liu S, Forrest J, Yang Y (2012) A brief introduction to grey systems theory. Grey Systems: Theory and Application 2(2):89–104CrossRefGoogle Scholar
  5. 5.
    Lin Y, Wang C, Wang J, Zheng D (2017) A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks[J]. Sensors 16(10):1–22Google Scholar
  6. 6.
    Ying Y, Li J, Chen Z, Guo J (2017) Study on rolling bearing on-line reliability analysis based on vibration information processing. Comput Electr Eng.  https://doi.org/10.1016/j.compeleceng.2017.11.029
  7. 7.
    Deng JL (1988) Grey System. China Ocean Press, BeijingGoogle Scholar
  8. 8.
    Wang Q (1989) The Grey Relational Analysis of B-Mode. Journal of Huazhong Universtiy of Science and Tenchnology 6:77–82MathSciNetGoogle Scholar
  9. 9.
    Zhenguo Mei (1992) The Concept and Computation Method of Grey Absolute Correlation Degree, Systems Engineering, Vole.5, pp.43–44+72Google Scholar
  10. 10.
    Wuxiang T (1995) The Concept and the Computation Method of T’s Correlation Degree. Application of Statistics and Management 1:34–37+33Google Scholar
  11. 11.
    Yaoguo D (1994) The Research of Grey Slope Relational Grade. System Sciences and comprehensive Studies In Agriculture, Supplement 10:331–337Google Scholar
  12. 12.
    Yugang S, Yaoguo D (2007) The Improved Model of Grey Slope Relational Grade. Statistics and Decision 15:12–13Google Scholar
  13. 13.
    Wang Q, Guo L (2005) Generalized Relational Analysis Method. Journal of Huazhong Unversity of Science and Technology (Natural Science Edition) 8:97–99MathSciNetGoogle Scholar
  14. 14.
    Shoaling Z (1996) Comparison between Computation Models of Grey Interconnect Degree and Analysis on Their Shourages. Syst Eng 3:45–49Google Scholar
  15. 15.
    Xuequan L (1995) Research On the Computation Model of Grey Interconnect Degree. Syst Eng 6:58–61Google Scholar
  16. 16.
    Mingliang L (1998) A New Descrimiant Byelaw for Grey Interconnect Degree and Its Calculation Formulas. Syst Eng 1:68–70+61Google Scholar
  17. 17.
    Lu F, Xiang L, Quan L (2000) The Theory of Gray Relative Analysis and It’s New Research. Journal of Wuhan university of technology 2:41–43+47Google Scholar
  18. 18.
    Naixiang S, Dong T, Zheng S (1992) On Several Theoretical Problems of Grey Correlation Degree. Syst Eng 6:23–26Google Scholar
  19. 19.
    Liang HY, Zonghai C (2003) The Inconsistent Problems in the Grey Relational Theory. Systems Engineering –Theory & Practice 8:118–121Google Scholar
  20. 20.
    Bose GK: “Selecting significant process parameters of ecg process using Fuzzy-MCDM technique,” International Journal of Materials Forming and Machining Processes (IJMFMP), 2, pp. 38-53(2015)CrossRefGoogle Scholar
  21. 21.
    Ying Y, Cao Y, Li S, Li J, Guo J (2016) Study on gas turbine engine fault diagnostic approach with a hybrid of gray relation theory and gas-path analysis. Advances in Mechanical Engineering 8(1):1–14Google Scholar
  22. 22.
    Hsu PF, Lin EP (2016) Tsai C W. Optimal Selection of Business Managers for Integrated Marketing Communications Companies Using AHP and GRA. International Journal of Customer Relationship Marketing and Management (IJCRMM) 7:16–29CrossRefGoogle Scholar
  23. 23.
    Chaoyang F, Zheng J, Zhao J (2001) Application of Grey Relational Analysis for Corrosion Failure of Oil Tubes. Corros Sci:881–889Google Scholar
  24. 24.
    Abhang LB, Hameedullah M (2012) Response surface modeling and grey relational analysis to optimize turning parameters with multiple performance characteristics. Internaional journal of manufacturing, materials 2:12–45Google Scholar
  25. 25.
    Otero AR, Ejnioui A, Otero CE, Tejay G (2011) Evaluation of information security controls in organizations by grey relational analysis. International Journal of Dependable and Trustworthy Information Systems (IJDTIS) 2:36–54CrossRefGoogle Scholar
  26. 26.
    Xu-bo L, XI-cai S, Man-jun L, Zhi-fu C (2010) Quick estimation to parameters of LPI radar-signals based on integral-envelope [J]. Systems Engineering and Electronics 10:2031–2035Google Scholar
  27. 27.
    Peng K, Zhang M, Li Q, et al (2016) Fiber optic perimeter detection based on principal component analysis[C]//Optical Communications and Networks (ICOCN), 2016 15th International Conference on. IEEE, 1–3Google Scholar
  28. 28.
    Zou H, Hastie T, Tibshirani R (2006) Sparse Principal Component Analysis[C]// British Machine Vision Conference 2006, Edinburgh, Uk, September. DBLP, 377–386Google Scholar
  29. 29.
    Deng J (2002) The Basic Method of Grey System Theory. Huazhong Unversity of Science and Technology Press, WuhanGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (CEMEE)LuoyangChina
  2. 2.Electronic Information CollegeShanghai Dianji UniversityShanghaiChina

Personalised recommendations