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

TL;DR

<3-5 sentence high-level summary> The paper tackles the instability and limited coverage of Wi-Fi sensing in real indoor environments by introducing a wall-reflection aware analytical model that incorporates both LoS and indoor wall-reflected paths. It shows that placing devices near walls within a certain range can substantially expand sensing coverage, and validates this with experiments on respiratory monitoring and stationary crowd counting, achieving up to 11.2% improvement in counting accuracy. The work fills a critical gap by linking deployment strategies to sensing performance, and offers deployment-aware guidance for practical indoor Wi-Fi sensing across various applications. Overall, the model provides a tractable framework to optimize device placement with respect to walls to enhance robustness and coverage in real-world environments.

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.

Figures (21)

• Figure 1: Enhancing sensing coverage through device deployment.
• Figure 2: Categorizing Wi-Fi signal propagation: static paths vs dynamic paths.
• Figure 3: Sensing model without walls.
• Figure 4: Wall-reflection sensing model. (a) Tx near the wall. (b) Rx near the wall. (c) Both Tx and Rx near walls.
• Figure 5: Heatmap of sensing capability in indoor environments.