Anomaly Detection Using WiFi Signals of Opportunity

Detection of changes in indoor areas and controlled environments is getting increasing interest in ambient intelligence and security. In this paper, we propose a radio-frequency (RF)based anomaly detector that, observing the spectrum received from signals of opportunity (SoOp) and exploiting machine learning (ML) techniques, is capable of revealing changes in an indoor environment. Based on real waveforms emitted by a WiFi access point (AP) and collected by a RF sensor, we demonstrate that anomaly detection, e.g., represented by the presence of a person in the monitored area, is possible. The proposed methodology, tested in a typical office environment when the AP-sensor link is in non-line-of-sight (NLOS), achieves an accuracy greater than 95% just by collecting few beacon packets, i.e., in dozens of milliseconds. Moreover, results demonstrate that the proposed approach outperforms a well-known received signal strength (RSS)-based solution in terms of accuracy, even using just a single sensor.