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

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Knowledge Science, Engineering and Management (KSEM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10412))

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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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Acknowledgement

This work was supported by Research of life cycle management and control system for equipment household registration, No. J770011104.

Author information

Authors and Affiliations

  1. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Dali Zhu, Na Pang, Weimiao Feng & Yuchen Ma

  2. School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China

    Dali Zhu, Na Pang, Weimiao Feng & Yuchen Ma

  3. School of Information Technology, Deakin University, Geelong, Australia

    Muhmmad Al-Khiza’ay

Authors
  1. Dali Zhu
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  2. Na Pang
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  3. Weimiao Feng
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  4. Muhmmad Al-Khiza’ay
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  5. Yuchen Ma
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Corresponding author

Correspondence to Na Pang .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, Victoria, Australia

    Gang Li

  2. University of Arizona, Tucson, Arizona, USA

    Yong Ge

  3. Southwest University, Chongqing, China

    Zili Zhang

  4. Peking University, Beijing, China

    Zhi Jin

  5. University of Technology Sydney, Sydney, New South Wales, Australia

    Michael Blumenstein

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Zhu, D., Pang, N., Feng, W., Al-Khiza’ay, M., Ma, Y. (2017). Device-Free Intruder Sensing Leveraging Fine-Grained Physical Layer Signatures. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-63558-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63557-6

  • Online ISBN: 978-3-319-63558-3

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