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2017 | OriginalPaper | Buchkapitel

Device-Free Intruder Sensing Leveraging Fine-Grained Physical Layer Signatures

verfasst von : Dali Zhu, Na Pang, Weimiao Feng, Muhmmad Al-Khiza’ay, Yuchen Ma

Erschienen in: Knowledge Science, Engineering and Management

Verlag: Springer International Publishing

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Abstract

With the development of smart indoor spaces, intruder sensing has attracted great attention in the past decades. Realtime intruder sensing in intelligent video surveillance is challenging due to the various covariate factors such as walking surface, clothing, carrying condition. Gait recognition provides a feasible approach for human identification. Pioneer systems usually rely on computer vision or wearable sensors which pose unacceptable privacy risks or be limited to additional devices. In this paper, we present CareFi, a device-free intruder sensing system that can identify a stranger or a burglar based on Commercial Off-The-Shelf (COTS) WiFi-enabled devices. CareFi extracts the fine-grained physical layer Channel State Information (CSI) to analyze the distinguishing gait characteristics for intruder sensing. CareFi can identify the intruder under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. CareFi does not require any dedicated sensors or lighting and works in dark just as well as in light. We prototype CareFi using commercial off-the-shelf WiFi devices and experimental results in typical indoor scenarios show that it achieves more than \(87.2\%\) detection rate for intruder sensing.

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Adib, F., Kabelac, Z., Katabi, D., Miller, R.C.: 3D tracking via body radio reflections. In: 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI 14), pp. 317–329 (2014)

Ailisto, H.J., Makela, S.M.: Identifying people from gait pattern with accelerometers. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 5779, pp. 7–14 (2005)

Al-Qaness, M.A.A., Li, F.: Wiger: WiFi-based gesture recognition system, vol. 5(6), p. 92 (2016)

Arora, P., Srivastava, S.: Gait recognition using gait Gaussian image. In: International Conference on Signal Processing and Integrated Networks, pp. 791–794 (2015)

Bagci, I.E., Roedig, U., Martinovic, I., Schulz, M., Hollick, M.: Using channel state information for tamper detection in the internet of things. In: ACSAC 2015 - The Computer Security Applications Conference, pp. 131–140 (2015)

Benabdelkader, C., Cutler, R.G.: Gait recognition using image self-similarity. EURASIP J. Adv. Sig. Process. 2004(4), 1–14 (2004)

Chang, J.Y., Lee, K.Y., Wei, Y.L., Lin, C.J., Hsu, W.: We can “see” you via WiFi - an overview and beyond (2016)

Halperin, D., Wenjun, H., Sheth, A., Wetherall, D.: Tool release: gathering 802.11n traces with channel state information. ACM Sigcomm Comput. Commun. Rev. 41(1), 53–53 (2011)CrossRef

Hu, M., Wang, Y., Zhang, Z., Zhang, D., Little, J.J.: Incremental learning for video-based gait recognition with LBP flow. IEEE Trans. Cybern. 43(1), 77–89 (2013)

10.

Juefei-Xu, F., Bhagavatula, C., Jaech, A., Prasad, U.: Gait-ID on the move: pace independent human identification using cell phone accelerometer dynamics. In: IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems, pp. 8–15 (2012)

11.

Kotaru, M., Katti, S.: Position tracking for virtual reality using commodity WiFi (2017). arXiv preprint arXiv:1703.03468

12.

Li, H., Yang, W., Wang, J., Xu, Y., Huang, L.: WiFinger: talk to your smart devices with finger-grained gesture. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 250–261. ACM (2016)

13.

Mantyjarvi, J., Lindholm, M., Vildjiounaite, E., Makela, S.M.: Identifying users of portable devices from gait pattern with accelerometers. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. ii/973–ii/976 (2005)

14.

Ngo, T.T., Makihara, Y., Nagahara, H., Mukaigawa, Y., Yagi, Y.: The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recogn. 47(1), 228–237 (2014)CrossRef

15.

Pan, S., Wang, N., Qian, Y., Velibeyoglu, I., Noh, H.Y., Zhang, P.: Indoor person identification through footstep induced structural vibration. In: International Workshop on Mobile Computing Systems and Applications, pp. 81–86 (2015)

16.

Qian, K., Wu, C., Zhou, Z., Zheng, Y., Yang, Z., Liu, Y.: Inferring motion direction using commodity WiFi for interactive exergames. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, pp. 1961–1972. ACM (2017)

17.

Radhakrishnan, M., Eswaran, S., Misra, A., Chander, D., Dasgupta, K.: Iris: Tapping wearable sensing to capture in-store retail insights on shoppers. In: IEEE International Conference on Pervasive Computing and Communications, pp. 1–8 (2016)

18.

Schwesig, R., Leuchte, S., Fischer, D., Ullmann, R., Kluttig, A.: Inertial sensor based reference gait data for healthy subjects. Gait Posture 33(4), 673–678 (2011)CrossRef

19.

Tahmoush, D., Silvious, J.: Radar micro-doppler for long range front-view gait recognition. In: IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2009)

20.

Wang, C., Zhang, J., Wang, L., Jian, P., Yuan, X.: Human identification using temporal information preserving gait template. IEEE Trans. Softw. Eng. 34(11), 2164 (2011)

21.

Wang, H., Zhang, D., Wang, Y., et al.: RT-Fall: a real-time and contactless fall detection system with commodity WiFi devices. IEEE Trans. Mob. Comput. 16(2), 1 (2017)

22.

Wang, W., Liu, A.X., Shahzad, M.: Gait recognition using WiFi signals. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 363–373. ACM (2016)

23.

Wang, W., Liu, A.X., Shahzad, M., Ling, K., Lu, S.: Understanding and modeling of WiFi signal based human activity recognition. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pp. 65–76. ACM (2015)

24.

Wang, Y., Liu, J., Chen, Y., Gruteser, M., Yang, J., Liu, H.: E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures. In: Proceedings of the 20th Annual International Conference on Mobile Computing and Networking, pp. 617–628. ACM (2014)

25.

Wang,Y., Fathy, A.E.: Micro-doppler signatures for intelligent human gait recognition using a UWB impulse radar. In: IEEE International Symposium on Antennas and Propagation (2011)

26.

Wang, Y., Wu, K., Ni, L.M.: WiFall: Device-free fall detection by wireless networks. IEEE Trans. Mobile Comput. 16(2), 581–594 (2017)CrossRef

27.

Wu, D., Zhang, D., Xu, C., Wang, Y., Wang, H.: Widir: walking direction estimation using wireless signals. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 351–362. ACM (2016)

28.

Xu, D., Yan, S., Tao, D., Lin, S., Zhang, H.J.: Marginal fisher analysis and its variants for human gait recognition and content-based image retrieval. IEEE Trans. Image Process. 16(11), 2811–2821 (2007)MathSciNetCrossRef

29.

Yu, S., Tan, D., Tan, T.: A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In:18th International Conference on Pattern Recognition, ICPR 2006, vol. 4, pp. 441–444. IEEE (2006)

30.

Zeng, Y., Pathak, P.H., Mohapatra, P.: WiWho: WiFi-based person identification in smart spaces. In: Proceedings of the 15th International Conference on Information Processing in Sensor Networks, p. 4. IEEE Press (2016)

31.

Zheng, X., Wang, J., Shangguan, L., Zhou, Z., Liu, Y.: Smokey: ubiquitous smoking detection with commercial WiFi infrastructures. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp. 1–9. IEEE (2016)

32.

Zhu, D., Pang, N., Li, G., Liu, S.: WiseFi: Activity localization and recognition on commodity off-the-shelf wi-Fi devices. In: 2016 IEEE 18th International Conference on High Performance Computing and Communications, IEEE 14th International Conference on Smart City, IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), pp. 562–569. IEEE (2016)

33.

Zhu, D., Pang, N., Li, G., Rong, W., Fan, Z.: WiN: Non-invasive abnormal activity detection leveraging ne-grained wi signals. In: Trustcom/BigDataSE/I SPA, 2016 IEEE, pp. 744–751. IEEE (2016)

Titel: Device-Free Intruder Sensing Leveraging Fine-Grained Physical Layer Signatures
verfasst von: Dali Zhu
Na Pang
Weimiao Feng
Muhmmad Al-Khiza’ay
Yuchen Ma
Verlag: Springer International Publishing
Buch: Knowledge Science, Engineering and Management
Print ISBN: 978-3-319-63557-6

Electronic ISBN: 978-3-319-63558-3

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-63558-3_16

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