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OMNeT++ Drone Simulation

A high-fidelity simulation framework for analyzing MQTT-based drone swarm networks using OMNeT++ and INET.

OMNeT++INET FrameworkC++NEDMQTTBashPython
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# 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.