Indoor Target Intrusion Detection via Iterative Transfer Learning Based Cognitive Sensing
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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.
KeywordsWLAN Intrusion detection Cross-validation Pseudo-label Iterative transfer learning
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).
The authors have contributed jointly to all parts on the preparation of this manuscript, and all authors read and approved the final manuscript.
Compliance with Ethical Standards
The authors declare that they have no competing interests.
- 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 4:1–11Google 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 6:21359–21368Google Scholar
- 8.Feng X (1997) Wireless local area network (WLAN). China Railway Society 26(27):79–80Google Scholar
- 9.Jia M, Yin Z, Guo Q, et al (2017) Downlink design for spectrum efficient IoT network. IEEE Internet Things JGoogle 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 181:1–13Google Scholar
- 14.Kosba AE, Saeed A, Youssef M (2012) Rasid: a robust wlan device-free passive motion detection system. In: IEEE Percom, pp 180–189Google Scholar
- 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 222–229Google Scholar
- 16.Shi S, Sigg S, Ji Y (2016) Probabilistic fingerprinting based passive device-free localization from channel state information. In: IEEE VTC, pp 1–5Google Scholar
- 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 1–9Google Scholar
- 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 1–7Google Scholar
- 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 1–6Google 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 8–14Google Scholar
- 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):138–150Google 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 1–6Google Scholar
- 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 1–9Google Scholar
- 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 43–50Google 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 70–84Google Scholar
- 29.Park J, Curtis D, Teller S, et al (2011) Implications of device diversity for organic localization. In: IEEE INFOCOM, pp 3182–3190Google Scholar
- 31.Fung GPC, Jeffrey XY, Lu H, et al. Text classification without negative examples revisit[J]. IEEE Trans Knowl Data Eng 2006(1):6–20Google 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 331–336Google Scholar
- 34.Blitzer J, McDonald R, Pereira F (2006) Domain adaptation with structural correspondence learning. In: Conference on empirical methods in natural language, pp 120–128Google Scholar
- 36.Wang J, Chen Y, Hu L, et al (2018) Stratified transfer learning for cross-domain activity recognition. In: IEEE Percom, pp 1–10Google 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 536–542Google Scholar
- 39.KreBel H-GU (2012) Pairwise classification and support vector machines. In: Conference on advances in kernel methods, pp 255–268Google Scholar