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Network Topology Optimiser

Network Topology Optimiser

License C Python PRs Welcome

An advanced network optimisation framework implementing modified MST algorithms for cellular tower placement

Features • Installation • Usage • Documentation • Contributing

📋 Overview

Network Topology Optimiser is a high-performance tool that implements modified versions of Kruskal's and Prim's algorithms to optimise cellular tower networks. The project focuses on handling real-world constraints such as signal interference and operational costs while maintaining optimal network coverage.

Key Features

  • 🚀 Efficient Implementation: Optimised MST algorithms for network analysis
  • 🎯 Dual Optimisation: Handles both interference and cost constraints
  • 📊 Visualization Tools: Python scripts for network topology analysis
  • 🔄 Flexible Input: Supports both sparse and dense network configurations

🛠️ Prerequisites

  • C Compiler (gcc recommended)
  • Python 3.x
  • Python packages: 
    pip install networkx matplotlib numpy pandas

🚀 Installation & Setup

  1. Clone the repository

    git clone https://github.com/Eusha425/network-topology-optimiser.git
cd network-topology-optimiser

  2. Compile the C code

    gcc -O2 -o optimiser main.c graph.c archive.c

  3. Install required Python packages

    pip install networkx matplotlib numpy pandas

📊 Usage

Input Format

The program expects input in the following format:

[number_of_vertices]
[source] [destination] [interference] [cost]
...

Running the Program

Using Input Redirection (Default Method)

# For dense networks
./optimiser < input.txt

# For sparse networks
./optimiser < input_sparse.txt

Alternative Method (File Reading)

If you prefer direct file reading instead of input redirection, you'll need to modify the C code to implement file reading functionality. The main sections that would need modification are:

  • main.c: Add file handling logic
  • graph.c: Modify input processing functions

Generating Test Data

python main_generator.py

Visualizing Results

python graph_visual.py    # General network visualization
python kruskal_visual.py  # Kruskal's algorithm results
python prim_visual.py     # Prim's algorithm results

📁 Project Structure

network-topology-optimiser/
├── src/
│   ├── main.c              # Main program implementation
│   ├── graph.c             # Graph implementation and algorithms
│   ├── graph.h             # Graph header file
│   └── archive.c           # Archive utility functions
├── visualization/
│   ├── graph_visual.py     # Network visualization script
│   ├── kruskal_visual.py   # Kruskal's algorithm visualization
│   └── prim_visual.py      # Prim's algorithm visualization
├── data/
│   ├── input.txt           # Dense graph dataset
│   └── input_sparse.txt    # Sparse graph dataset
└── main_generator.py       # Test data generator

📈 Performance Analysis

Algorithm Comparison

Network Type Vertices Kruskal (ms) Prim (ms)
Sparse 1000 45 89
Dense 1000 156 78

🤝 Contributing

We love your input! We want to make contributing to Network Topology Optimiser as easy and transparent as possible, whether it's:

  • Reporting a bug
  • Discussing the current state of the code
  • Submitting a fix
  • Proposing new features
  • Becoming a maintainer

Check out our Contributing Guidelines for ways to get started.

Please refer to our Code of Conduct for details on our code of conduct.

📜 License

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