Indoor people density sensing using Wi-Fi and channel state information

Abstract

Device-free passive crowd estimation technologies are capable of measuring the density of people in an area, using existing wireless network infrastructure. It has been applied in various application domains such as pedestrian control, crowd management in subways, guided tours and so forth. In this work, we have designed, implemented and validated a device-free indoor human crowd density sensing method based on Channel State Information (CSI) captured by a single Wi-Fi receiver. We investigate the behaviour of the CSI amplitude variance of each receiving stream over the different subcarriers and propose a method to aggregate the CSI amplitude over time without losing critical information. Further, we evaluated the method using three different machine learning algorithms. The result shows the proposed method achieves an estimated accuracy of 99.8% with the Weighted K-Nearest Neighbour.