Library Seat Vacancy Tracker

Overview

This project estimates how crowded the library is using Wi-Fi data and mobile devices. It collects information from routers and user phones, then shows real-time seat availability on a heatmap.

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Goals

• Count devices connected to library Wi-Fi.
• Predict crowd density and trends.
• Visualize occupancy on a live map.
• Keep data private (anonymized, auto-deleted).

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How It Works

1. Wi-Fi Scans

   • Use nmap to find devices per router.
   • Switch between SSIDs to cover all areas.
   • Send results (SSID, MAC, signal strength) to backend.

2. Mobile Data (Optional)

   • Android/iOS app records connected SSID + background location.
   • Data is hashed, timestamped, and synced to backend.
   • Helps validate Wi-Fi scans.

3. Filtering & Accuracy

   • Ignore personal hotspots.
   • Add confidence scores where data is sparse.

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Outputs

• Real-time device count per zone.
• Heatmap of library occupancy.
• Occupancy confidence level.

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Tech Stack

• Mobile App: React Native (Expo, TypeScript)
• Backend: Node.js/Go + PostgreSQL
• Scanning: nmap, Wi-Fi scripts
• Visualization: Mapbox

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Next Steps

• Phase 1: Basic Wi-Fi scanning + backend storage
• Phase 2: Add mobile app for extra data
• Phase 3: Improve filtering and confidence