Infrastructure-Aware Named-Data Networking for Autonomous Vehicular Communication

⚠️ Disclaimer: This is a custom, unsupported fork of the ndnSIM 2.x simulator.

Overview

This repository contains the codebase for the paper titled "Infrastructure-Aware Named-Data Networking for Autonomous Vehicular Communication". We have open-sourced this project under the GNU General Public License v2.0 to ensure the reproducibility of our published results and to encourage further academic exploration.

Prerequisites

This project has been tested and verified on the following platforms:

• Ubuntu 22.04 (amd64)
• Ubuntu 24.04 (amd64)

1. SUMO Setup

Step 1: Install Dependencies

sudo apt update
sudo apt-get install cmake python3 g++ libxerces-c-dev libfox-1.6-dev libgdal-dev libproj-dev libgl2ps-dev swig

Step 2: Clone and Build SUMO

cd $HOME
git clone --recursive https://github.com/eclipse/sumo
export SUMO_HOME="$HOME/sumo"
mkdir -p sumo/build_config/cmake-build
cd sumo/build_config/cmake-build
cmake ../..
make -j$(nproc)

Step 3: Configure Environment

Add the following to your ~/.bashrc:

export SUMO_HOME="$HOME/sumo"
export TRACE_EXPORTER="$SUMO_HOME/tools/traceExporter.py"
export PATH=$PATH:"$SUMO_HOME/bin"

Apply the changes:

source ~/.bashrc

2. Setup

Step 1: Install Dependencies

sudo apt install build-essential libsqlite3-dev libboost-all-dev libssl-dev git python3-setuptools castxml

Step 2: Clone and Build

git clone https://github.com/solvedbiscuit71/CAFE.git
cd CAFE
CXXFLAGS="-std=c++17" ./waf configure --disable-python --disable-examples
./waf

3. NetAnim Setup (Optional)

Step 1: Install Dependencies

sudo apt install -y mercurial qtcreator qtbase5-dev qtchooser qt5-qmake cmake qtbase5-dev-tools

Step 2: Build NetAnim

cd netanim
make clean
qmake NetAnim.pro
make

Generate results

To generate the table 4 presented in the paper, we have provided the logs and traces files in the data folder, run the following commands:

Step 1: Install Dependencies

python3 -m venv .venv
source .venv/bin/active
pip install scipy

Step 2: Run Script

cd data
python generate_table4.py

Simulate results

To simulate and generate the logs and traces in the data folder, run the provided scenario script:

mkdir trace # sumo will generate .tcl files into trace/ folder
mkdir log   # simulation log will be placed inside log/ folder
python scripts/scene.py

Note on Broadcast Flooding

By default, the simulation includes geobroadcast logic. To replicate the broadcast flooding baseline, you must manually disable the geobroadcast logic:

1. Open src/ndnSIM/NFD/daemon/fw/caf-forwarder.cpp.
2. Comment out the relevant geobroadcast logic sections.
3. Re-run ./waf and the simulation script.