WiFi Dead Zone Mapper

Map WiFi signal strength throughout your home and visualize dead zones with heat maps.

• 📊 Collect WiFi signal strength data as you move around
• 🗺️ Generate beautiful heat map visualizations
• 📍 Manual position tracking (works better than GPS indoors)
• 🔍 Identify dead zones and areas with weak coverage
• 📈 3D surface visualization option
• 💾 Save and resume data collection sessions

Install the required Python packages:

pip install numpy matplotlib scipy --break-system-packages

The tool automatically detects your operating system and uses the appropriate WiFi command:

• Linux: Uses iwconfig (usually pre-installed)
• macOS: Uses airport utility (built-in)
• Windows: Uses netsh wlan (built-in)

Run the data collection script and walk around your home:

python3 wifi_mapper.py

Instructions:

1. Choose a reference point (e.g., your front door = 0,0)
2. Walk to different locations in your home
3. At each location, press Enter to log the signal
4. Enter your position relative to the reference point in meters
5. Optionally label the room/area
6. Type 'done' when finished

Tips:

• Collect at least 10-15 points for a good heat map
• Sample from different rooms and areas
• Include spots where you know WiFi is weak
• You can run the script multiple times - data accumulates

Example session:

Point #1 - Press Enter to log signal: [Enter]
X coordinate (meters from reference): 0
Y coordinate (meters from reference): 0
Room/Area name (optional): Living Room
✓ Logged: Signal = -45 dBm (Excellent) at position (0, 0)

Point #2 - Press Enter to log signal: [Enter]
X coordinate (meters from reference): 5
Y coordinate (meters from reference): 3
Room/Area name (optional): Kitchen
✓ Logged: Signal = -67 dBm (Fair) at position (5, 3)

Create visualizations from your collected data:

python3 wifi_visualizer.py

This will:

• Analyze your data for dead zones
• Generate a heat map (wifi_heatmap.png)
• Show signal quality statistics

Advanced options:

# Use custom data file
python3 wifi_visualizer.py --data my_wifi_data.json

# Save heat map to specific file
python3 wifi_visualizer.py --output my_heatmap.png

# Also create 3D visualization
python3 wifi_visualizer.py --3d

# Change dead zone threshold (default: -75 dBm)
python3 wifi_visualizer.py --threshold -70

WiFi signal strength is measured in dBm (decibel-milliwatts):

• wifi_data.json - Your collected data points (can be backed up/shared)
• wifi_heatmap.png - 2D heat map visualization
• wifi_3d.png - 3D surface plot (if using --3d flag)

1. Collect more data: 20-30 points give better interpolation
2. Cover the space evenly: Don't cluster all points in one area
3. Multiple floors: Run separately for each floor
4. Time of day: WiFi can vary with interference - test at different times
5. Router placement: Use the heat map to optimize router location

Based on your heat map, you can:

1. Reposition your router: Move it closer to dead zones
2. Elevate the router: Higher placement often improves coverage
3. Reduce obstacles: Remove metal objects, mirrors, or dense furniture between router and dead zones
4. Add a WiFi extender: Place it in areas with fair coverage to boost dead zones
5. Upgrade to mesh WiFi: For large homes with multiple dead zones

"Could not detect WiFi signal"

• Make sure you're connected to WiFi
• On Linux, install wireless-tools: sudo apt-get install wireless-tools
• On macOS, airport utility should be built-in
• Try running with sudo: sudo python3 wifi_mapper.py

"No data points found"

• Make sure you've run wifi_mapper.py first
• Check that wifi_data.json exists in the current directory

Heat map looks strange

• Collect more data points (aim for 15+)
• Make sure points are spread across your space
• Check for any outlier measurements

# 1. Install dependencies
pip install numpy matplotlib scipy --break-system-packages

# 2. Collect data (walk around and log 15-20 points)
python3 wifi_mapper.py

# 3. Generate heat map
python3 wifi_visualizer.py --3d

# 4. View your heat maps
# Open wifi_heatmap.png and wifi_3d.png

The wifi_data.json file stores data in this format:

[
  {
    "timestamp": "2024-02-04T10:30:00",
    "signal_dbm": -55,
    "x": 0,
    "y": 0,
    "room": "Living Room"
  },
  {
    "timestamp": "2024-02-04T10:32:00",
    "signal_dbm": -72,
    "x": 5,
    "y": 3,
    "room": "Kitchen"
  }
]

You can manually edit this file or import data from other sources.

Free to use and modify. No warranty provided.