Mm- Humidity: Fine-Grained Humidity Sensing with Millimeter Wave Signals

Abstract

Atmospheric humidity is a significantly important factor in our daily lives, as it is closely bound up with agriculture, industrial production, human health and so on. Therefore, efficient and precise humidity measurement techniques are indispensable. However, the existing off-the-shelf techniques, including the dry and wet bulb hygrometer, humidity sensor as well as WiFi based detector, all fail to achieve a sensitive, accurate and convenient humidity measurement, especial for a large scale deployment. In this paper, we observe that different levels of water vapor have certain impact on millimeter wave (mmWave) signals in indoor environments. Accordingly, we propose an fine-grained environmental humidity sensing technology via wireless signals in the mmWave band. However, mmWave signals are not only sensitive to humidity, but also other environmental factors, such as oxygen. To establish a linear relationship between humidity and mm Wave signal propagation, we exploit a subspace projection technique to remove the environmental noise. Upon extracting the humidity-associated features in the noise-free signal, we utilize support vector machine (SVM) to model the humidity measurement classifier of a certain place. Extensive experiments have been conducted in different scenarios in order to verify the effectiveness of the proposed system. Results show that the average accuracy of humidity measurement is up to 85 % when the humidity interval is 3 %, and is 95 % when the humidity interval is 5%. We further show that the proposed method is very sensitive to the humidity dynamics and is 63.2 times faster compared to the traditional hygrometers.