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

2024-12-03

AI Summary

This work addresses the lack of theoretical guidance for optimal device placement in indoor Wi-Fi sensing. We present the first systematic modeling of how wall distance affects multipath channel characteristics and sensing performance. A novel theoretical model is proposed that jointly incorporates line-of-sight (LoS) and wall-reflected propagation paths, revealing the physical mechanism by which near-wall deployment extends sensing coverage. We formally define the “optimal near-wall deployment interval” as a new design concept. Through analytical superposition of LoS and reflected components—validated experimentally on real-world breathing monitoring and stationary crowd counting tasks—our approach improves counting accuracy by 11.2%. This study fills a critical gap in the fundamental understanding of indoor wireless sensing coverage and establishes a quantifiable, reusable optimization paradigm for Wi-Fi sensing device deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Applications
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Effect of wall-device distance on Wi-Fi sensing coverage
• Impact of walls on dynamic sensing in indoor environments
• Optimizing transmitter and receiver placement near walls

Innovation

• Model incorporates wall-reflected and LoS paths
• Strategic device placement near walls improves coverage
• Demonstrated 11.2% accuracy boost in applications