Wireless Network Performance Analysis with ns-3

This repository contains the results and source code for analyzing wireless network performance using ns-3. Two primary experiments are included:

1. Node Density Analysis: Examining the impact of varying node densities on network metrics such as Packet Delivery Ratio (PDR), end-to-end delay, and throughput.
2. Traffic Load Analysis: Studying how different traffic loads affect the same performance metrics.

Repository Structure

.
├── node_density
│   ├── Task1.cc.cc               # ns-3 simulation script for node density analysis
│   ├── Task1.py.py               # Python script for processing trace files
│   ├── metrics_vs_node_density1.png # Graph showing results of node density experiment
│   ├── test1.sh                  # Shell script to run the node density experiment
├── traffic_load
│   ├── Task2.cc.cc               # ns-3 simulation script for traffic load analysis
│   ├── Task2.py.py               # Python script for processing trace files
│   ├── metrics_vs_traffic_load2.png # Graph showing results of traffic load experiment
│   ├── test2.sh                  # Shell script to run the traffic load experiment
├── LICENSE
├── README.md                     # This README file

Prerequisites

• ns-3: Ensure that ns-3 is installed and properly configured on your system. Follow the official ns-3 installation guide for instructions.
• Python: Required to run the result processing scripts (Task1.py.py and Task2.py.py).

Task 1: Node Density Analysis

Objective: Analyze the impact of varying node densities (10 to 100) on network performance metrics.

Methodology

• Networks with node densities ranging from 10 to 100 were created.
• Each density was tested ten times with seeds from 1 to 10 to ensure randomness.
• A Python script processed trace files to compute the following metrics:
  • PDR: Number of received packets divided by the number of sent packets.
  • End-to-End Delay: Averaged over the times of packet transmission and reception.
  • Throughput: Total received packets divided by the simulation duration.

Results

The results indicate:

• Higher Node Densities improve PDR and throughput while reducing end-to-end delay.
• Example metrics:
  • At 10 nodes: PDR ~9%, Delay ~8.44s, Throughput ~208 kbps.
  • At 100 nodes: PDR ~99%, Delay ~7.43s, Throughput ~6730 kbps.

Task 2: Traffic Load Analysis

Objective: Evaluate the effect of varying traffic loads (1 to 30 source-destination pairs) on network performance metrics in an 80-node network.

Methodology

• Traffic loads were varied from 1 to 30 pairs.
• Each scenario was tested ten times with different seeds for reliability.
• A Python script processed trace files to compute:
  • PDR: Number of received packets divided by the number of sent packets.
  • End-to-End Delay: Averaged over the times of packet transmission and reception.
  • Throughput: Total received packets divided by the simulation duration.

Results

The results show:

• Higher Traffic Loads decrease PDR and slightly increase delay but significantly improve throughput.
• Example metrics:
  • At traffic load 1: PDR ~78.77%, Delay ~7.81s, Throughput ~37489.65 kbps.
  • At traffic load 30: PDR ~48.84%, Delay ~8.61s, Throughput ~251995.99 kbps.

License

This project is licensed under the terms specified in the LICENSE file.

Contact

For questions or feedback, please open an issue or contact the repository owner.