Skip to main content

Advertisement

SpringerLink
Log in

Indoor Target Intrusion Detection via Iterative Transfer Learning Based Cognitive Sensing

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

The traditional localization technology, which requires the target carrying device and participating in localization process, transmits the signal to be received by the device to estimate the target locations, but it perceives the changes in the environment weakly as well as limits the application of localization services. Based on this, we propose a new indoor target intrusion detection approach based on iterative transfer learning without special device. In concrete terms, this approach relies on iterative transfer learning to use the signal received by Monitor Points (MPs) to determine whether there is a target intrusion in the environment, infer the area where the target is located, and consequently achieve autonomous cognitive sensing of environmental change. Specifically, first of all, we use the Received Signal Strength (RSS) data collected offline and their corresponding silence and intrusion labels to construct a source domain. Second, the cross-validation is applied to perform preliminary calibration on the RSS data collected online to obtain their corresponding pseudo-labels, and then these pseudo-labels are utilized to construct the target domain. Finally, the labels of target domain are obtained through the iterative intra-class transfer learning between the source and target domains. Furthermore, the experimental results show that the proposed approach can not only achieve high intrusion detection accuracy with a small number of RSS data, but also perform well in the cognitive sensing of the change of MPs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. In our testing, there are in total 4 areas of interest to be considered with target intrusion.

References

  1. Jia M, Gu X, Guo Q, et al (2016) Broadband hybrid satellite-terrestrial communication systems based on cognitive radio toward 5g. IEEE Wirel Commun 23(6):96–106

    Article  Google Scholar 

  2. Jia M, Liu X, Guo X, et al (2017) Joint cooperative spectrum sensing and channel selection optimization for satellite communication systems based on cognitive radio. Int J Satell Commun Netw 35(2):139–150

    Article  MathSciNet  Google Scholar 

  3. Jia M, Liu X, Yin Z, et al (2017) Joint cooperative spectrum sensing and spectrum opportunity for satellite cluster communication networks. Ad Hoc Netw 58(C):231–238

    Article  Google Scholar 

  4. Zhang Z, Zeng T, Yu X, et al (2017) Social-aware d2d pairing for cooperative video transmission using matching theory. Mobile Networks and Applications 4:1–11

    Google Scholar 

  5. Huang X, Shi L, Zhang C, et al (2017) Distributed resource allocation with imperfect spectrum sensing information and channel uncertainty in cognitive femtocell networks. EURASIP J Wirel Commun Netw 1:201

    Article  Google Scholar 

  6. Huang X, Tang S, Zheng Q, et al (2018) Dynamic femtocell gnb on/off strategies and seamless dual connectivity in 5g heterogeneous cellular networks. EURASIP J Wirel Commun Netw 6:21359–21368

    Google Scholar 

  7. Liu X, Li F, Na Z (2017) Optimal resource allocation in simultaneous cooperative spectrum sensing and energy harvesting for multichannel cognitive radio. IEEE Access 5:3801–3812

    Article  Google Scholar 

  8. Feng X (1997) Wireless local area network (WLAN). China Railway Society 26(27):79–80

    Google Scholar 

  9. Jia M, Yin Z, Guo Q, et al (2017) Downlink design for spectrum efficient IoT network. IEEE Internet Things J

  10. Tan W, Matthaiou M, Jin S, et al (2017) Spectral efficiency of dft based processing hybrid architectures in massive mimo. IEEE Wireless Commun Lett 6(5):586–589

    Article  Google Scholar 

  11. Tan W, Jin S, Wen CK, et al (2017) Spectral efficiency of multi user millimeter wave systems under single path with uniform rectangular arrays. EURASIP Wirel Commun Netw 181:1–13

    Google Scholar 

  12. Tan W, Xie D, Xia J, et al (2018) Spectral and energy efficiency of massive mimo for hybrid architectures based on phase shifters. IEEE Access 6:11751–11759

    Article  Google Scholar 

  13. Gao Q, Wang J, Wang H, et al (2012) Device-free localization with wireless networks based on compressive sensing. IET Commun 6(15):2395–2403

    Article  MathSciNet  Google Scholar 

  14. Kosba AE, Saeed A, Youssef M (2012) Rasid: a robust wlan device-free passive motion detection system. In: IEEE Percom, pp 180–189

  15. Youssef M, Mah M, Agrawala A (2007) Challenges: Device-free passive localization for wireless environments. In: ACM International conference on mobile computing and networking, pp 222–229

  16. Shi S, Sigg S, Ji Y (2016) Probabilistic fingerprinting based passive device-free localization from channel state information. In: IEEE VTC, pp 1–5

  17. Zhou R, Chen J, Lu X, et al (2017) Csi fingerprinting with svm regression to achieve device-free passive localization. In: IEEE WoWMom, pp 1–9

  18. Suryatali A, Dharmadhikari VB (2015) Computer vision based vehicle detection for toll collection system using embedded Linux. In: IEEE International conference on circuit, power and computing technologies, pp 1–7

  19. Foubert N, McKee AM, Goubran RA, et al (2012) Lying and sitting posture recognition and transition detection using a pressure sensor array. In: IEEE International symposium on medical measurements and applications proceedings, pp 1–6

  20. Saeed A, Kosba AE, Youssef M (2014) Ichnaea: a low-overhead robust wlan device-free passive localization system. IEEE J Sel Top Sign Proces 8(1):5–15

    Article  Google Scholar 

  21. Murillo AC, Gutierrez-Gomez D, Rituerto A, et al (2012) Wearable omnidirectional vision system for personal localization and guidance. In: IEEE Computer society conference on computer vision and pattern recognition workshops, pp 8–14

  22. Kari B, Kvarstein B, Hagen R, et al (2014) Pelvic floor muscle exercise for the treatment of female stress urinary incontinence: Reliability of vaginal pressure measurements of pelvic floor muscle strength. Neurourol Urodyn 111(38):138–150

    Google Scholar 

  23. Wilson J, Patwari N (2010) Radio tomographic imaging with wireless networks. IEEE Trans Mob Comput 9(5):621–632

    Article  Google Scholar 

  24. Moussa M, Youssef M (2009) Smart devices for smart environments: device-free passive detection in real environments. In: IEEE International conference on pervasive computing and communications, pp 1–6

  25. Chen X, Ma C, Allegue M, et al (2017) Taming the inconsistency of Wi-Fi fingerprints for device-free passive indoor localization. In: IEEE Conference on computer communications, pp 1–9

  26. Seifeldin M, El-keyi AF, Youssef M (2011) Kalman filter-based tracking of a device-free passive entity in wireless environments. In: ACM International workshop on wireless network testbeds, experimental evaluation and characterization, pp 43–50

  27. Seifeldin M, Saeed A, Kosba AE, et al (2013) Nuzzer: a large-scale device-free passive localization system for wireless environments. IEEE Trans Mob Comput 12(7):1321–1334

    Article  Google Scholar 

  28. Haeberlen A, Flannery E, Ladd AM, et al (2004) Practical robust localization over large-scale 802.11 wireless networks. In: ACM International conference on mobile computing and networking, pp 70–84

  29. Park J, Curtis D, Teller S, et al (2011) Implications of device diversity for organic localization. In: IEEE INFOCOM, pp 3182–3190

  30. Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  31. Fung GPC, Jeffrey XY, Lu H, et al. Text classification without negative examples revisit[J]. IEEE Trans Knowl Data Eng 2006(1):6–20

  32. Al-Mubaid H, Umair SA (2006) A new text categorization technique using distributional clustering and learning logic. IEEE Trans Knowl Data Eng 18(9):1156–1165

    Article  Google Scholar 

  33. Sun Z, Chen Y, Qi J, et al (2008) Adaptive localization through transfer learning in indoor Wi-Fi environment. In: Conference on machine learning and applications, pp 331–336

  34. Blitzer J, McDonald R, Pereira F (2006) Domain adaptation with structural correspondence learning. In: Conference on empirical methods in natural language, pp 120–128

  35. Pan SJ, Tsang IW, Kwok JT, et al (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 20(2):199–210

    Article  Google Scholar 

  36. Wang J, Chen Y, Hu L, et al (2018) Stratified transfer learning for cross-domain activity recognition. In: IEEE Percom, pp 1–10

  37. Gretton A, Borgwardt KM, Rasch MJ, et al (2012) A kernel two-sample test[J]. J Mach Learn Res 13(Mar):723–773

    MathSciNet  MATH  Google Scholar 

  38. Mika S, Smola A, Scholz M (1999) Kernel pca and de-noising in feature spaces. In: Conference on advances in neural information processing systems II, pp 536–542

  39. KreBel H-GU (2012) Pairwise classification and support vector machines. In: Conference on advances in kernel methods, pp 255–268

  40. Deak G, Curran K, Condell J, et al (2014) Detection of multi-occupancy using device-free passive localization. IET Wireless Sensor Systems 4(3):130–137

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported in part by the National Natural Science Foundation of China (61771083, 61704015), Program for Changjiang Scholars and Innovative Research Team in University (IRT1299),Postgraduate Scientific Research and Innovation Project of Chongqing (CYS18240,CYS17221), Special Fund of Chongqing Key Laboratory (CSTC), Fundamental Science and Frontier Technology Research Project of Chongqing (cstc2017jcyjAX0380, cstc2015jcyjBX0065), Scientific and Technological Research Foundation of Chongqing Municipal Education Commission (KJ1704083), and University Outstanding Achievement Transformation Project of Chongqing (KJZH17117).

Author information

Authors and Affiliations

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Mu Zhou, Yaoping Li, Yanmeng Wang & Zengshan Tian

  2. College of Information and Communication Engineering, Harbin Engineering University, 150001, Harbin, China

    Zhian Deng

  3. School of Computer Science and Technology, Nanjing Tech University, 210014, Nanjing, China

    Yongliang Sun

Authors
  1. Mu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yaoping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhian Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongliang Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yanmeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zengshan Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors have contributed jointly to all parts on the preparation of this manuscript, and all authors read and approved the final manuscript.

Corresponding author

Correspondence to Yaoping Li.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, M., Li, Y., Deng, Z. et al. Indoor Target Intrusion Detection via Iterative Transfer Learning Based Cognitive Sensing. Mobile Netw Appl 24, 2002–2013 (2019). https://doi.org/10.1007/s11036-019-01335-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-019-01335-2

Keywords

Search

Navigation