Advertisement

An Adaptive Passive Radio Map Construction for Indoor WLAN Intrusion Detection

  • Yixin LinEmail author
  • Wei Nie
  • Mu Zhou
  • Yong Wang
  • Zengshan Tian
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

Indoor WLAN intrusion detection technique for the anonymous target has been widely applied in many fields such as the smart home management, security monitoring, counterterrorism, and disaster relief. However, the existing indoor WLAN intrusion detection systems usually require constructing a passive radio map involving a lot of manpower and time cost, which is a significant barrier of the deployment of WLAN intrusion detection systems. In this paper, we propose to use the adaptive-depth ray tree model to automatically construct an adaptive passive radio map for indoor WLAN intrusion detection. In concrete terms, the quasi-3D ray-tracing model is enhanced by using the genetic algorithm to predict the received signal strength (RSS) propagation feature under the indoor silence and intrusion scenarios, which improves the computational efficiency while preserving the accuracy of passive radio map. Then, the RSS mean, variance, maximum, minimum, range, and median are allied to increase the robustness of passive radio map. Finally, we conduct empirical evaluations on the real-world data to validate the high intrusion detection rate and low database construction cost of the proposed method.

Keywords

Indoor intrusion detection Adaptive ray-tracing Passive radio map Genetic algorithm WLAN 

Notes

Acknowledgments

This work is supported in part by the Fundamental Science and Frontier Technology Research Project of Chongqing (cstc2017jcyjAX0380).

References

  1. 1.
    Youssef M, Mah M, Agrawala A. Challenges: device-free passive localization for wireless environments. In: ACM international conference on mobile computing and networking; 2007. p. 222–9.Google Scholar
  2. 2.
    Jin S, Choi S. A seamless handoff with multiple radios in IEEE 802.11 WLAN. IEEE Trans Veh Technol. 2014;63(3):1408–18.CrossRefGoogle Scholar
  3. 3.
    Wang Q, Yigitler H, Jantti R, et al. Localizing multiple objects using radio tomographic imaging technology. IEEE Trans Veh Technol. 2016;65(5):3641–56.CrossRefGoogle Scholar
  4. 4.
    Deak G, Curran K, Condell J, et al. Detection of multi-occupancy using device-free passive localization. IET Wirel Sens Syst. 2014;4(3):130–7.CrossRefGoogle Scholar
  5. 5.
    Liu Z, Guo L, Tao W. Full automatic preprocessing of digital map for 2.5D ray tracing propagation model in urban microcellular environment. Waves Random Complex Media. 2013;23(3):267–78.CrossRefGoogle Scholar
  6. 6.
    Jong YLCD, Herben MAHJ. Prediction of local mean power using 2-D ray-tracing-based propagation models. IEEE Trans Veh Technol. 2001;50(1):325–31.CrossRefGoogle Scholar
  7. 7.
    Sabar NR, Ayob M, Kendall G, et al. A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems. IEEE Trans Cybern. 2015;45(2):217–28.CrossRefGoogle Scholar
  8. 8.
    Erceg V, Rustako AJ, Roman R. Diffraction around corners and its effects on the microcell coverage area in urban and suburban environments at 900 MHz, 2 GHz, and 6 GHz. IEEE Trans Veh Technol. 1994;43(3):762–6.CrossRefGoogle Scholar
  9. 9.
    Dutt V, Chaudhry V, Khan I. Different approaches in pattern recognition. Comput Sci Eng. 2011;1(2):32–5.CrossRefGoogle Scholar
  10. 10.
    Queiroz A, Trintinalia LC. An analysis of human body shadowing models for ray-tracing radio channel characterization; 2016. p. 1–5.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Yixin Lin
    • 1
    Email author
  • Wei Nie
    • 1
  • Mu Zhou
    • 1
  • Yong Wang
    • 1
  • Zengshan Tian
    • 1
  1. 1.Chongqing Key Lab of Mobile Communications TechnologyChongqing University of Posts and TelecommunicationsChongqingChina

Personalised recommendations