AR-Alarm: An adaptive and robust intrusion detection system leveraging CSI from commodity Wi-Fi

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.