LAUSR

Currently, a major issue of WiFi-based sensing technologies is how to adapt to changes in the surrounding environment. The extreme sensitivity of Channel State Information (CSI) makes many WiFi sensing arts frustrated when applied to the complex and unknown real world. To solve this problem, in this paper, we propose freeEnv designed to automatically identify the micro-environmental changes (even tiny movements of the laptop) using WiFi devices, which can coexist with other WiFi sensing tasks with zero effort. To achieve automatic identification of micro-environmental changes, we quantify micro-environmental changes based on the physical propagation laws of WiFi signals and the main factors that affect CSI measurements. Then, we design a micro-environmental changes identification method, which determines whether the environment has changed by calculating the Earth Mover’s Distance (EMD) of the Probability Density Function (PDF) of continuous CSI, without requiring training data. To remove the influence of dynamic human behaviors, we design a human dynamic detection scheme, which is achieved by obtaining the average inter-cluster distance of performing Gaussian Mixture Model (GMM) clustering on CSI. We evaluate freeEnv in real-world scenarios with six different hardware, four different scenarios, and twenty-four ways of micro-environmental changes. The results show that our method is robust to different devices and scenarios, and can achieve the average precision of 96.1% and 93.2% for micro-environmental changes identification and human dynamic behavior detection. By testing on a case study of threshold-based human presence detection, freeEnv can effectively improve the detection performance.