🚀 Project Roadmap: Smart Wi-Fi Based Occupancy & Navigation System
📌 Objective:
Build a system that uses Wi-Fi signal data, device scans, and location services to: • Monitor real-time crowd density • Display signal strength heatmaps • Predict occupancy trends • Enable indoor navigation (optional AR)
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🛠️ Phase 1: Wi-Fi Mapping & Device Tracking (MVP)
🎯 Goal:
Estimate crowd density by tracking devices connected to routers.
📋 Tasks: • Use nmap to scan for connected devices per SSID • Automate SSID switching (script to hop across known routers) • Collect SSID + MAC + signal strength • Visualize with heatmap (using Wigle.net or Mapbox) • Basic backend setup to store scan results
✅ Output: • Real-time device count per location • Visual heatmap of Wi-Fi coverage
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📡 Phase 2: Mobile-Based Passive Data Collection
🎯 Goal:
Use mobile devices to enhance and validate network scan data.
📋 Tasks: • Android app to gather: • Connected SSID • Background location (low-power mode) • iOS equivalent using Shortcuts or native app • Sync data to backend via API • Anonymize data using device hash/fingerprint • Include timestamps and auto-expiry of data for privacy
✅ Output: • Crowd source device presence beyond what nmap can detect • Create mobility patterns inside mapped spaces
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🚧 Phase 3: Edge Cases & Error Handling
🎯 Goal:
Improve data quality & avoid false positives.
📋 Tasks: • Filter out personal hotspots using: • MAC address vendor prefixes • Unusual traffic patterns • Handle users without app installed • Display confidence level in occupancy estimate • Investigate feasibility of EACCESS or advanced filtering to identify SSID types
✅ Output: • More reliable data • Marked zones with “Low Confidence” when data is sparse
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🔮 Phase 4: Predictive Queueing & Forecast System
🎯 Goal:
Forecast near-future occupancy to help users plan their visits.
📋 Tasks: • Analyze past occupancy data for time-based trends • Optional “I’m Planning to Go” button for crowdsource forecasting • Use time-series analysis for trend prediction • Show low/med/high crowd estimate with time sliders
✅ Output: • Forecast panel with time-of-day heatmap • Optional user input queue system (opt-in only)
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🧭 Phase 5: Real-time Indoor Navigation (AR Mode)
🎯 Goal:
Help users navigate within the building using visual cues.
📋 Tasks: • Map the interior using floorplans or grid-style sections • Use ARCore (Android) or Unity-based AR to overlay directions • Use Wi-Fi or BLE triangulation for approximate position • Link destination to live density to redirect to emptier spaces
✅ Output: • Arrow-based navigation • Visual cues that adapt to real-time crowd levels
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📦 Tech Stack Summary
| Layer | Tools |
|---|---|
| Network Scanning | nmap, airmon-ng, Bash/Python |
| Backend | Django / FastAPI |
| Frontend | React / Next.js / Vue |
| Heatmap & Maps | Mapbox / Leaflet.js / Wigle.net |
| Mobile App | Kotlin (Android) / Swift (iOS) |
| AR Navigation | Unity3D + ARCore / ARKit |
| Data Storage | PostgreSQL / Firebase / TimescaleDB |
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💡 Future Enhancements (Post-MVP) • Voice assistant mode: “Where’s the quietest place right now?” • AI optimization of router placement using signal patterns • Integration with library booking or entry systems • Public dashboard for admin insights
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