Experience Paper: Scaling WiFi Sensing to Millions of Commodity Devices for Ubiquitous Home Monitoring

2025-06-04

AI Summary

To address four key challenges in deploying WiFi sensing for home monitoring on millions of commercial heterogeneous devices (e.g., routers, smart bulbs)—high false alarms from non-human motion, hardware-induced CSI variability, motion ambiguity across multiple users, and constrained edge compute and bandwidth—this paper proposes the first lightweight, robust sensing architecture natively integrating communication and sensing for large-scale heterogeneous deployments. Our approach comprises: (1) a CSI-based adaptive noise suppression model; (2) cross-device feature normalization to mitigate hardware heterogeneity; (3) an edge-cloud collaborative lightweight inference framework; and (4) a dynamic bandwidth-aware CSI compression mechanism. Extensive real-world evaluation across 280 edge devices, 16 scenarios, and over 4 million samples demonstrates 92.61% activity recognition accuracy, reduces non-human motion false alarms from 63.1% to 8.4%, and cuts CSI transmission overhead by 99.72%.

Technology Category

• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Machine Learning: Learning on the Edge & Model Compression
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

WiFi-based home monitoring has emerged as a compelling alternative to traditional camera- and sensor-based solutions, offering wide coverage with minimal intrusion by leveraging existing wireless infrastructure. This paper presents key insights and lessons learned from developing and deploying a large-scale WiFi sensing solution, currently operational across over 10 million commodity off-the-shelf routers and 100 million smart bulbs worldwide. Through this extensive deployment, we identify four real-world challenges that hinder the practical adoption of prior research: 1) Non-human movements (e.g., pets) frequently trigger false positives; 2) Low-cost WiFi chipsets and heterogeneous hardware introduce inconsistencies in channel state information (CSI) measurements; 3) Motion interference in multi-user environments complicates occupant differentiation; 4) Computational constraints on edge devices and limited cloud transmission impede real-time processing. To address these challenges, we present a practical and scalable system, validated through comprehensive two-year evaluations involving 280 edge devices, across 16 scenarios, and over 4 million motion samples. Our solutions achieve an accuracy of 92.61% in diverse real-world homes while reducing false alarms due to non-human movements from 63.1% to 8.4% and lowering CSI transmission overhead by 99.72%. Notably, our system integrates sensing and communication, supporting simultaneous WiFi sensing and data transmission over home WiFi networks. While focused on home monitoring, our findings and strategies generalize to various WiFi sensing applications. By bridging the gaps between theoretical research and commercial deployment, this work offers practical insights for scaling WiFi sensing in real-world environments.

Problem

• Reducing false positives from non-human movements in WiFi sensing
• Addressing inconsistencies in CSI due to low-cost heterogeneous hardware
• Overcoming motion interference in multi-user environments for occupant differentiation

Innovation

• Leverages existing WiFi infrastructure for home monitoring
• Reduces false alarms and CSI transmission overhead
• Integrates sensing and communication on home networks