WiFi Sensing Toolkit for Urban Studies

Introduction

This book provides a complete guide to building and using WiFi sensors for measuring pedestrian activity in urban environments. It covers hardware setup, data processing, and extracting five key metrics: Location, Count, Track, Revisits, and Activities.

Why WiFi Sensing?

WiFi sensing concept: sensors mounted on buildings detect probe requests broadcast by WiFi-enabled devices carried by pedestrians.

Quantifying pedestrian traffic is essential for urban planning, public safety, and sustainable city development. Traditional methods (manual counts or surveys) are labor-intensive and provide only snapshots. WiFi sensing offers continuous, non-invasive monitoring by detecting probe requests that WiFi-enabled devices broadcast regularly to discover nearby networks.

Most pedestrians carry smartphones or other WiFi devices. Each device’s probe request includes a MAC address, enabling sensors to detect presence, estimate location, and track movement across multiple sensors, though modern MAC address randomization constrains this (Appendix B — MAC Address Randomization). This passive approach requires no participation from pedestrians, making it practical for monitoring public spaces at scale.

Recent urban sensing initiatives, such as the Array of Things in Chicago and S-DoT in Seoul, demonstrate the growing interest in sensor-based urban analytics. This book makes WiFi sensing accessible to researchers, planners, and practitioners using affordable, off-the-shelf components.

Passive vs Active WiFi Sensing

WiFi-based location sensing takes two forms:

Passive sensing answers “Where is the device?” Sensors (sniffers) receive probe requests broadcast by nearby devices. The device holder doesn’t need to do anything: their phone automatically sends these packets. Detection range is typically 30–100 meters outdoors. This book focuses on passive sensing for pedestrian monitoring.

Active sensing answers “Where am I?” from the device’s perspective. The device scans for nearby access points and sends the data to a server for location estimation. This requires user consent and an installed app, making it impractical for monitoring public spaces.

Passive sensing trades individual-level precision for scalability. It captures aggregate patterns (when and where people concentrate, how they move through space) without requiring consent from every passerby.

What This Book Covers

This book follows the full pipeline, from raw hardware to urban insight:

• Building the Sensor: assemble a Raspberry Pi sensor, put its WiFi card in monitor mode, and deploy it in the field.
• Processing Data: turn raw probe-request packets into clean, analysis-ready records.
• Extracting Metrics: derive five metrics from the cleaned data (Location, Count, Track, Revisits, and Activities).
• Demonstration: apply the pipeline to a commercial district near the University of Ulsan, asking where people stay and how one-time and returning visitors differ.

Three appendices cover practical deployment notes, the mechanics and limits of MAC address randomization, and a five-deployment field record of the randomization transition (2019–2024).

References

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