Wall-Proximity Matters: Understanding the Effect of Device Placement with Respect to the Wall for Indoor Wi-Fi Sensing

TL;DR

This paper introduces a theoretical model that highlights the importance of device placement near walls to enhance Wi-Fi sensing coverage and accuracy in indoor environments, addressing practical deployment challenges.

Contribution

It presents a novel sensing coverage model considering wall effects, guiding optimal device placement for improved indoor Wi-Fi sensing performance.

Findings

11.2% improvement in counting accuracy

Wall proximity significantly expands sensing coverage

Model validated through respiratory and crowd counting experiments

Abstract

Wi-Fi sensing has been extensively explored for various applications, including vital sign monitoring, human activity recognition, indoor localization, and tracking. However, practical implementation in real-world scenarios is hindered by unstable sensing performance and limited knowledge of wireless sensing coverage. While previous works have aimed to address these challenges, they have overlooked the impact of walls on dynamic sensing capabilities in indoor environments. To fill this gap, we present a theoretical model that accounts for the effect of wall-device distance on sensing coverage. By incorporating both the wall-reflected path and the line-of-sight (LoS) path for dynamic signals, we develop a comprehensive sensing coverage model tailored for indoor environments. This model demonstrates that strategically deploying the transmitter and receiver in proximity to the wall within a specific range can significantly expand sensing coverage. We assess the performance of our model through experiments in respiratory monitoring and stationary crowd counting applications, showcasing a notable 11.2% improvement in counting accuracy. These findings pave the way for optimized deployment strategies in Wi-Fi sensing, facilitating more effective and accurate sensing solutions across various applications.