Abstract
Device-free human intrusion detection holds great potential and multiple challenges for applications ranging from asset protection to elder care. In this paper, leveraging the fine-grained Channel State Information (CSI) in commodity WiFi devices, we design and implement an adaptive and robust human intrusion detection system, called AR-Alarm. By utilizing a robust feature and self-adaptive learning mechanism, AR-Alarm achieves real-time intrusion detection in different environments without calibration efforts. To further increase the system robustness, we propose a few novel methods to distinguish real human intrusion from object motion in daily life such as object dropping, curtain swinging and pets moving. As demonstrated in the experiments, AR-Alarm achieves a high detection rate and low false alarm rate.
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Acknowledgments
This work is supported by National Key Research and Development Plan under Grant No. 2016YFB1001200.
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Li, S., Li, X., Niu, K., Wang, H., Zhang, Y., Zhang, D. (2017). AR-Alarm: An Adaptive and Robust Intrusion Detection System Leveraging CSI from Commodity Wi-Fi. In: Mokhtari, M., Abdulrazak, B., Aloulou, H. (eds) Enhanced Quality of Life and Smart Living. ICOST 2017. Lecture Notes in Computer Science(), vol 10461. Springer, Cham. https://doi.org/10.1007/978-3-319-66188-9_18
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DOI: https://doi.org/10.1007/978-3-319-66188-9_18
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