OMNeT++ Drone Simulation

OMNeT++ Drone Simulation

A specialized research tool designed to model, simulate, and analyze the performance of MQTT-based communication protocols in dynamic drone networks.

Project Overview

This workspace provides a rigorous environment for testing drone network architectures without the risks and costs of physical deployment. Built on top of the industry-standard OMNeT++ discrete event simulator and the INET framework, it allows researchers to model complex behaviors including battery drain, wireless interference, and protocol latency in IoT scenarios.

Technologies Used

• Core Engine: OMNeT++ 6.2.0
• Network Stack: INET Framework 4.5
• Language: C++ (C++14), NED (Network Description Language)
• Protocol: MQTT
• Scripting: Bash, Python

Key Features

1. Realistic Mobility Models: Simulates drone movement patterns and their impact on signal strength and connectivity.
2. Energy Profiling: Detailed battery consumption modeling for drones based on flight dynamics and transmission power.
3. MQTT Protocol Integration: Full implementation of the Publish-Subscribe model suited for unstable, high-latency network environments.
4. Data Analytics: Automated collection of scalar and vector results for bandwidth, latency, and packet loss analysis.

Project Details

The simulation is structured around three core node types: Drones (publishers), Gateways (brokers), and Control Centers (subscribers). It was developed to support research into "Adaptive Neural Compression," optimizing how data is transmitted in bandwidth-constrained aerial networks.

Challenges

• Protocol Fidelity: Accurately replicating the behavior of the MQTT protocol within a discrete event simulation environment.
• Resource Modeling: Creating realistic constraints for battery life and wireless transmission power to ensure simulation validity.
• Configuration Management: Managing complex permutation of network parameters (latency, bandwidth, packet size) for comparative analysis.

Solutions

• Modular Architecture: Designed distinct NED modules for Drones, Gateways, and Control Centers, allowing independent logic implementation in C++.
• Custom NED Topologies: Created flexible network description files that allow for easy scaling of drone swarm sizes.
• Automated Workflows: Developed shell scripts (main.sh) to automate the build process and execution of multiple simulation scenarios.

Conclusion

The Simulation Workspace bridges the gap between theoretical network protocols and practical drone deployment, offering a robust platform for validating IoT communication strategies before they take flight.