WiFi-Based Adaptive Indoor Passive Intrusion Detection

Abstract

Passive intrusion detection, which is an emerging technique to detect whether there exists any intruders in monitored area, is widely used in home security and smart home, etc. Up to now, various indoor fine-grained passive human intrusion detection systems using WiFi signals have been proposed. However, those existing detection systems mostly rely on elaborate off-line training process, which hampers fast deployment of wireless devices and also reduces system robustness. To response those problems, in this paper, we propose APID, a system for adaptive indoor passive intrusion detection, which enables adaptive, device-free human intrusion detection in indoor environments using channel state information (CSI) of WiFi signals. Firstly, APID evaluates dispersion of CSI amplitude, which is not affected by the mean amplitude. Secondly, APID extracts CSI amplitude dispersion ratio between two adjacent time windows as sensitive metrics for intrusion detection. Then, the hypothesis testing is utilized to achieve no-calibration human motion detection. Finally, we implement APID on the commodity WiFi devices and evaluate it in two typical indoor scenarios. The experimental results show that APID can achieve an average detection accuracy of more than 96%.