Smart CSI Processing for Accruate Commodity WiFi-based Humidity Sensing

TL;DR

This paper presents an improved WiFi-based humidity sensing framework that leverages advanced filtering and machine learning to significantly enhance sensing accuracy to 97%, enabling scalable and low-cost indoor humidity monitoring.

Contribution

The paper introduces a novel filtering and data processing approach combined with ML algorithms to improve WiFi-based humidity sensing accuracy using commodity hardware.

Findings

Achieved 97% humidity sensing accuracy.

Enhanced de-noising improves CSI signal quality.

Effective ML integration for humidity detection.

Abstract

Indoor humidity is a crucial factor affecting people's health and well-being. Wireless humidity sensing techniques are scalable and low-cost, making them a promising solution for measuring humidity in indoor environments without requiring additional devices. Such, machine learning (ML) assisted WiFi sensing is being envisioned as the key enabler for integrated sensing and communication (ISAC). However, the current WiFi-based sensing systems, such as WiHumidity, suffer from low accuracy. We propose an enhanced WiFi-based humidity detection framework to address this issue that utilizes innovative filtering and data processing techniques to exploit humidity-specific channel state information (CSI) signatures during RF sensing. These signals are then fed into ML algorithms for detecting different humidity levels. Specifically, our improved de-noising solution for the CSI captured by commodity hardware for WiFi sensing, combined with the k-th nearest neighbour ML algorithm and resolution tuning technique, helps improve humidity sensing accuracy. Our commercially available hardware-based experiments provide insights into achievable sensing resolution. Our empirical investigation shows that our enhanced framework can improve the accuracy of humidity sensing to 97%.