This repository contains the code for the second homework of the Autonomous Networking course at the University of La Sapienza, Rome.

DroNET is a Python based simulator for experimenting routing algorithms and mobility models on unmanned aerial vehicle networks.

The project is developed using Python 3.10. To install the required packages, you can use the provided conda environment:

conda env create -f environment.yml

In order to start the simulator you can run the following command:

conda activate droNET
python -m src.main

The project has the following structure:

The entry point of the project is the src.main file, from there you can run simulations and extensive experimental campaigns, by setting up an appropriate src.simulator.Simulator object.

The two main directories are data and src. The directory data contains all the data of the project, like drones tours, and other input and output of the project. The directory src contains the source code, organized in several packages.

• src.drawing it contains all the classes needed for drawing the simulation on screen. Typically, you may want to get your hands in this directory if you want to change the aspect of the simulation, display a new object, or label on the area.

• src.entites it contains all the classes that define the behaviour and the structure of the main entities of the project like: Drone, Depot, Environment, Packet, Event classes.

• src.experiments it contains classes that handle experimental campaigns.

• src.plots it contains classes to perform plotting operations

• src.routing_algorithms it contains all the classes modelling the several routing algorithms, every routing algorithm should have its own class.

• src.simulation it contains all the classes to handle a simulation and its metrics.

• src.utilities it contains all the utilities and the configuration parameters. In particular use src.utilities.config file to specify all the constants and parameters for a one-shot simulation, ideal when one wants to evaluate the quality of a routing algorithm making frequent executions. Constants and parameters should always be added here and never be hard-coded.

The current version of the simulator is free for non-commercial use.

This project is based on the paper A Q-Learning-Based Topology-Aware Routing Protocol for Flying Ad Hoc Networks

The simulator was done by Andrea Coletta in collaboration with Matteo Prata, PhD Student at La Sapienza coletta[AT]di.uniroma1.it, prata[AT]di.uniroma1.it and later extended by Flavio Giorgi flavio.giorgi[AT]uniroma1.it and Giulio Attenni giulio.attenni[AT]uniroma1.it.