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🌐 Federated Sensor Network (FedSense)

License: MIT Python 3.8+

A framework for privacy-preserving federated learning in sensor networks. This project demonstrates advanced concepts in pattern recognition, privacy preservation, and distributed learning systems.

Simulation Preview

🌟 Key Features

Pattern Recognition

  • Dynamic Pattern Detection: Real-time analysis of sensor readings
  • Event Impact Analysis: Intelligent event-based pattern recognition
  • Adaptive Learning: Continuous pattern refinement and accuracy improvement

Privacy Preservation

  • Privacy Metrics: Real-time privacy score tracking
  • Data Protection: Minimal raw data sharing
  • Pattern Anonymization: Secure pattern sharing mechanisms

Advanced Analytics

  • Real-time Visualization: Dynamic multi-plot analysis
  • Pattern Library: Centralized pattern knowledge base
  • Performance Metrics: Comprehensive accuracy tracking

🚀 Quick Start

Installation

cd federated-sensor-network

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install the package
pip install -e .

Basic Usage

from fedsense.network import EnhancedFederatedNetwork

# Create and run a simulation
network = EnhancedFederatedNetwork()
network.run()

🎮 Advanced Usage

Custom Sensor Configuration

from fedsense.core import EnhancedSensor
import numpy as np

# Initialize custom sensor
sensor = EnhancedSensor(
    name="Custom Sensor",
    location=(0, 0),
    pattern_type="factory"
)

# Configure learning parameters
sensor.base_temp = 25.0
sensor.add_known_event("custom_event", 
    start_hour=10, 
    duration=2
)

Pattern Analysis

# Analyze patterns with custom window
patterns = sensor.learn_patterns(window_size=48)

# Access specific pattern metrics
daily_range = patterns['daily_range']
variance = patterns['variance']
trend = patterns['trend']
peak_hours = patterns['peak_hours']

🔧 Technical Details

Core Components

  1. Sensor Systems

    • Temperature pattern generation
    • Event impact modeling
    • Local pattern learning

  2. Privacy Framework

    • Privacy score calculation
    • Data sharing controls
    • Pattern anonymization

  3. Federated Learning

    • Distributed pattern recognition
    • Global knowledge aggregation
    • Model synchronization

Performance Features

  • Real-time pattern analysis
  • Dynamic visualization updates
  • Privacy-preserving data sharing
  • Neural network-based learning

📊 Applications

  • Industrial IoT: Factory sensor networks
  • Smart Buildings: HVAC optimization
  • Environmental Monitoring: Weather pattern analysis
  • Privacy Research: Data protection studies
  • Distributed Systems: Federated learning research

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • Neural network components powered by PyTorch
  • Visualization built on Matplotlib
  • Console interface using Rich
  • Special thanks to all contributors and the open-source community