Objective
Build a machine learning model that predicts if a wireless client will roam to a new Access Point within the next few seconds, based on the changing signal strength (RSSI) from surrounding APs. This predictive capability enables proactive network optimization and seamless handover experiences.
Business Value
For Network Engineers transitioning to ML/AI:- Proactive Network Management: Predict and prepare for client roaming before it happens
- Quality of Experience: Improve voice and video call quality by reducing handover latency
- Network Optimization: Optimize AP placement and power settings based on roaming patterns
- Resource Planning: Anticipate capacity needs across different access points
- Troubleshooting: Identify problematic areas where clients frequently struggle to roam
Real-World Applications:- Enterprise Wi-Fi network optimization
- Fast roaming (802.11r) implementation
- Hospital and industrial IoT device connectivity
- Campus and large facility Wi-Fi management
- Venue-based customer experience enhancement
Core Libraries
- pandas & numpy: Data manipulation and time-series analysis of RSSI values
- scikit-learn: Random Forest classifier for roaming prediction
- matplotlib & seaborn: Visualization of signal patterns and prediction results
Dataset
Synthetically Generated Wi-Fi Roaming Data- Source: Simulated mobile device movement through multi-AP environment
- Size: 300 time steps representing device mobility patterns
- Features: RSSI values from multiple APs, rate of change (delta) features
- Target: Binary prediction of roaming events within prediction window
Key Features:- rssi_AP_1, rssi_AP_2, rssi_AP_3: Signal strength from each access point
- rssi_AP_X_delta: Rate of change in signal strength for each AP
- will_roam_soon: Target variable indicating if roaming will occur soon
Project Structure
018_WiFi_Roaming_Prediction/
├── README.md
├── wifi_roaming_prediction.ipynb
└── requirements.txt
Step-by-Step Guide
1. Environment Setup
Create and activate a virtual environment:
python -m venv wifi_roaming_env
source wifi_roaming_env/bin/activate # On Windows: wifi_roaming_env\Scripts\activate
pip install -r requirements.txt
2. Synthetic Data Generation
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
# Simulation parameters
time_steps = 300 # 300 seconds of data
num_aps = 3
aps = [f'AP_{i+1}' for i in range(num_aps)]
# Simulate mobile device walking through building
for t in range(time_steps):
# Simulate RSSI values (in dBm, higher is better)
rssi_ap1 = -30 - (t * 0.2) + np.random.normal(0, 2)
rssi_ap2 = -90 + (abs(t - 150) * -0.4) + 50 + np.random.normal(0, 2)
rssi_ap3 = -90 + (abs(t - 250) * -0.4) + 40 + np.random.normal(0, 2)
3. Feature Engineering
# Create predictive target variable
prediction_window = 5 # seconds
df['will_roam_soon'] = 0
# Shift connected_ap to see future connections
df['next_ap'] = df['connected_ap'].shift(-prediction_window)
# Identify roaming events
roam_indices = df[df['connected_ap'] != df['next_ap']].index
# Set flag for time steps leading up to roam
for idx in roam_indices:
df.loc[max(0, idx - prediction_window):idx, 'will_roam_soon'] = 1
# Engineer rate-of-change features
for ap in aps:
df[f'rssi_{ap}_delta'] = df[f'rssi_{ap}'].diff().fillna(0)
4. Model Training
# Prepare features and target
feature_cols = [col for col in df.columns if 'rssi' in col]
X = df[feature_cols]
y = df['will_roam_soon']
# Time-series split for realistic evaluation
split_point = int(len(df) * 0.7)
X_train, X_test = X.iloc[:split_point], X.iloc[split_point:]
y_train, y_test = y.iloc[:split_point], y.iloc[split_point:]
# Train Random Forest with balanced classes
model = RandomForestClassifier(n_estimators=100, random_state=42, class_weight='balanced')
model.fit(X_train, y_train)
5. Model Evaluation
from sklearn.metrics import classification_report, confusion_matrix
# Predict on test set
y_pred = model.predict(X_test)
# Focus on recall for roaming events
print(classification_report(y_test, y_pred, target_names=['Will Not Roam', 'Will Roam']))
# Confusion matrix visualization
cm = confusion_matrix(y_test, y_pred)
sns.heatmap(cm, annot=True, fmt='d', cmap='Greens')
6. Visualization and Analysis
import matplotlib.pyplot as plt
# Plot RSSI patterns and predictions
plt.figure(figsize=(16, 8))
# Plot RSSI values from all APs
for ap in aps:
plt.plot(df_test_results['time'], df_test_results[f'rssi_{ap}'], label=f'RSSI {ap}')
# Highlight prediction windows
plt.fill_between(df_test_results['time'], -90, -20,
where=df_test_results['will_roam_soon']==1,
facecolor='orange', alpha=0.5, label='Actual Pre-Roam Window')
# Show model predictions
plt.scatter(df_test_results['time'][df_test_results['prediction']==1],
df_test_results['rssi_AP_3'][df_test_results['prediction']==1] + 2,
color='red', marker='v', s=50, label='Predicted Roam')
Success Criteria
1. High Recall: Achieve >80% recall for roaming events (minimize missed roaming predictions)
2. Acceptable Precision: Maintain >60% precision to avoid excessive false alarms
3. Timing Accuracy: Predict roaming events 3-7 seconds in advance
4. Feature Importance: Signal strength delta should be among top features
5. Real-time Capability: Model prediction time <100ms for production deployment
Key Insights
Technical Learnings:- Time-Series Prediction: Handle temporal patterns in wireless signal data
- Feature Engineering: Rate of change features often more predictive than absolute values
- Class Imbalance: Roaming events are rare, requiring balanced training approaches
- Temporal Validation: Use time-aware splitting for realistic performance assessment
Network Engineering Applications:- Fast Roaming: Pre-authenticate clients with likely destination APs
- Load Balancing: Proactively manage client distribution across APs
- Coverage Optimization: Identify areas where roaming is problematic
- Capacity Planning: Predict load patterns for different access points
Next Steps & Extensions
Immediate Improvements
1. Multi-Device Tracking: Extend to predict roaming for multiple concurrent clients
2. Environmental Factors: Include additional features like device type, application usage
3. Real-time Integration: Connect to live Wi-Fi controller APIs for real data
4. Adaptive Windows: Dynamic prediction windows based on device mobility patterns
Advanced Features
1. Deep Learning Models: Implement LSTM/GRU networks for complex temporal patterns
2. Location-Aware Predictions: Incorporate floor plans and physical layout information
3. Quality Metrics: Predict not just roaming, but roaming success probability
4. Multi-Band Prediction: Handle 2.4GHz/5GHz/6GHz band steering decisions
Production Deployment
1. Controller Integration: Deploy models on wireless LAN controllers
2. Edge Computing: Implement lightweight models on access points themselves
3. API Development: Create REST APIs for network management system integration
4. Monitoring Dashboard: Real-time visualization of roaming predictions and patterns
Research Extensions
1. Federated Learning: Train models across multiple sites while preserving privacy
2. Reinforcement Learning: Optimize AP power and channel assignments based on roaming patterns
3. Cross-Protocol Analysis: Combine Wi-Fi with cellular handover prediction models
4. Anomaly Detection: Identify unusual roaming patterns that may indicate security issues
Industry-Specific Applications
1. Healthcare: Ensure critical medical device connectivity during roaming
2. Manufacturing: Maintain industrial IoT device connections on factory floors
3. Education: Optimize campus-wide Wi-Fi for student and faculty mobility
4. Retail: Enhance customer Wi-Fi experience in large shopping centers
Implementation Considerations
Performance Optimization
- Feature Selection: Use only the most predictive RSSI and delta features
- Model Complexity: Balance accuracy with inference speed for real-time deployment
- Memory Usage: Optimize for deployment on resource-constrained network equipment
- Batch Processing: Handle multiple client predictions efficiently
Network Integration
- SNMP Integration: Pull RSSI data from existing network monitoring systems
- Controller APIs: Integrate with vendor-specific wireless controller platforms
- Standards Compliance: Ensure compatibility with 802.11 standards and amendments
- Vendor Agnostic: Design for compatibility across different Wi-Fi equipment vendors
This project demonstrates how predictive analytics can transform reactive network management into proactive optimization, providing network engineers with powerful tools to enhance wireless network performance and user experience.