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.
- 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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Wi-Fi Scans
- Use
nmapto find devices per router. - Switch between SSIDs to cover all areas.
- Send results (SSID, MAC, signal strength) to backend.
- Use
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Mobile Data (Optional)
- Android/iOS app records connected SSID + background location.
- Data is hashed, timestamped, and synced to backend.
- Helps validate Wi-Fi scans.
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Filtering & Accuracy
- Ignore personal hotspots.
- Add confidence scores where data is sparse.
- Real-time device count per zone.
- Heatmap of library occupancy.
- Occupancy confidence level.
- Mobile App: React Native (Expo, TypeScript)
- Backend: Node.js/Go + PostgreSQL
- Scanning: nmap, Wi-Fi scripts
- Visualization: Mapbox
- Phase 1: Basic Wi-Fi scanning + backend storage
- Phase 2: Add mobile app for extra data
- Phase 3: Improve filtering and confidence