Vanet Simulation

The way of carrying out a comparative analysis is determined as both challenging and fascinating. We offer an extensive instruction that support you to perform a comparative analysis employing Vehicular Ad-Hoc Network (VANET) simulations in an efficient manner:

Step 1: Define Goals

The objectives of our comparative analysis have to be described. Typically, usual goals could encompass:

  • Consider various routing protocols and contrast their effectiveness.
  • The influence of differing vehicle loads has to be assessed.
  • Aim to evaluate the performance of various safety technologies.
  • Under various network situations, focus on investigating QoS parameters.

Step 2: Choose Simulation Tools

In order to assist VANETs, we select suitable and efficient simulation tools. Some of the prominent tools are:

  • OMNeT++ with Veins: Along with vehicular mobility, it offers extensive network simulation.
  • NS-3: It is described as a discrete-event network simulator along with VANET modules.
  • SUMO: For designing practical vehicle mobility, SUMO could be employed.
  • MATLAB: The MATLAB simulation tool is utilized for data analysis and conventional simulations.

Step 3: Determine Scenarios and Parameters

For our simulations, we specify the metrics and settings, like:

  • Network Size: It includes road network size, number of vehicles.
  • Vehicle Mobility: Typically, traffic trends, momentum, direction are encompassed.
  • Communication Range: It denotes transmission range of vehicles.
  • Traffic Density: Low, medium, and high-density settings are involved here by us.
  • Simulation Time: This parameter indicates the duration of every executed simulation.

Step 4: Implement Simulation Models

In the selected tools, our team configure our simulation models:

  1. Vehicle Mobility:
  • As a means to develop practical traffic settings, aim to employ SUMO.
  • For utilizing mobility traces in network simulators such as NS-3, OMNeT++, it is better to transfer them.
  1. Network Configuration:
  • The network parameters such as MAC protocols, transmission range have to be arranged.
  • It is approachable to deploy various safety technologies and routing protocols.

Step 5: Execute Simulations

For every setting, focus on executing simulations. Based on performance parameters, we gather data. Few of the usual parameters are:

  • Packet Delivery Ratio (PDR): It assesses the ratio of successfully delivered packets to transmitted packets.
  • End-to-End Delay: This metric is referred to as average time taken for a packet to traverse from source to destination.
  • Throughput: Total quantity of data delivered across the network in an efficient manner could be evaluated.
  • Routing Overhead: It is defined as the number of control messages that are produced by routing protocols.
  • Collision Rate: Generally, it indicates the frequency of packet collisions in the network.

Step 6: Analyze Outcomes

In order to contrast the effectiveness of various settings, examine the gathered data. Typically, to explain the outcomes, we employ visualization tools and statistical techniques:

  1. Statistical Analysis:
  • For every parameter, evaluate variance, mean, and standard deviation.
  • As a means to describe the relevance of variations, it is appreciable to employ hypothesis testing.
  1. Visualization:
  • To visualize performance parameters, we plan to plot charts and graphs.
  • Focus on utilizing tools such as Excel, MATLAB, or Python with libraries like Seaborn, Matplotlib.

Step 7: Interpret and Discuss Outcomes

From our exploration, we describe the outcomes. The following points has to be determined:

  • Protocol Comparison: In what way do various protocols work under different situations? Which protocol is considered as more effective and why?
  • Impact of Density: In what way does vehicle load impact network effectiveness? Are few protocols more resistant to high density?
  • Security Mechanisms: How efficient are the safety technologies in securing the network? Do they initiate relevant overhead?
  • QoS Metrics: Which settings offer effective QoS? In what way do various metrics impact QoS?

Instance Comparative Analysis Outline

  1. Introduction
  • We include aims and relevance of the comparative analysis.
  1. Simulation Setup
  • This section encompasses the explanation of arrangement and tools.
  • Metrics and settings.
  1. Performance Metrics
  • Generally, explanations and significance of every parameter are involved.
  1. Results and Analysis
  • Our team includes statistical analysis and visualization of outcomes.
  • Comparison of settings/protocols.
  1. Discussion
  • Explanation of outcomes.
  • Perceptions and impacts are encompassed in this segment.
  1. Conclusion
  • Finally, in this section, aim to involve the outline of results.
  • Beneficial suggestions for upcoming investigation.

Instance Comparative Analysis

In a VANET setting with differing vehicle loads, we determine a comparative analysis of three routing protocols such as GPSR, AODV, and DSR.

Simulation Setup

  • Network Size: 100 vehicles.
  • Mobility Model: Consider a SUMO-generated urban setting.
  • Transmission Range: 250 meters.
  • Traffic Density: High (200 vehicles/km²), Medium (100 vehicles/km²), Low (50 vehicles/km²).
  • Simulation Time: 600 seconds.

Performance Metrics

  • End-to-End delay
  • Routing overhead
  • Packet Delivery Ratio (PDR)
  • Throughput

Results and Analysis

  1. Packet Delivery Ratio:
  • In low-density settings, AODV displays higher PDR.
  • Typically, in high-density settings, GPSR functions in an effective manner because of its position-based routing.
  1. End-to-End Delay:
  • DSR has higher delay in high-density but less delay in low-density settings.
  • Among loads, GPSR sustains coherent delay.
  1. Throughput:
  • In medium-density settings, GPSR and AODV have the same throughput.
  • Because of the enhanced route detection time, DSR demonstrates lower throughput in high-density.
  1. Routing Overhead:
  • Typically, in high-density settings, AODV has higher overhead.
  • Because of its dependence on geographical data, GPSR has the least overhead.

How to simulate vanet projects using NS3?

Simulating the VANET project is examined as intriguing as well as a little bit complicated task. We suggest a thorough instruction based on how to simulate VANET projects utilizing NS-3:

Step 1: Set Up NS-3 Environment

  1. Install NS-3:
  • It is advisable to download NS-3 from the official website: NS-3 Download.
  • The installation guidelines for our operating system have to be adhered to. Specifically, we employ the following commands for Linux:

sudo apt update

sudo apt install -y build-essential autoconf automake libxmu-dev


tar xjf ns-allinone-3.33.tar.bz2

cd ns-allinone-3.33

./ –enable-examples –enable-tests

  1. Verify Installation:
  • Through executing an instance script, we make sure that the NS-3 is installed in a proper manner.

cd ns-3.33

./waf –run hello-simulator

Step 2: Define the Simulation Scenario

  1. Network Topology:
  • Focus on determining the communication range, number of vehicles, and road design.
  1. Mobility Model:
  • As a means to specify the mobility of vehicles, it is beneficial to utilize the MobilityHelper class in NS-3. On the other hand, for practical vehicular mobility, aim to combine SUMO.
  1. Communication Model:
  • The communication metrics like MAC and PHY layers have to be described. Mainly, for VANET interaction, our team employs WaveNetDevice.

Step 3: Implement the Simulation Script

  1. Create a New Script:
  • In the scratch directory, we plan to develop a novel script file (For instance.,
  1. Include Necessary Headers:
  • For mobility, network devices, and protocols, focus on encompassing the essential NS-3 headers.

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/mobility-module.h”

#include “ns3/wave-module.h”

#include “ns3/internet-module.h”

#include “ns3/applications-module.h”

using namespace ns3;

  1. Set Up the Simulation:
  • We intend to specify the main function and configure the network nodes, mobility, and devices.

int main (int argc, char *argv[]) {

    // Create nodes

    NodeContainer vehicles;

    vehicles.Create(50); // Number of vehicles

    // Set up mobility model

    MobilityHelper mobility;    mobility.SetMobilityModel(“ns3::ConstantVelocityMobilityModel”);


    // Set initial positions and velocities

    for (uint32_t i = 0; i < vehicles.GetN(); ++i) {        Ptr<ConstantVelocityMobilityModel> mobilityModel = vehicles.Get(i)->GetObject<ConstantVelocityMobilityModel>();

        mobilityModel->SetPosition(Vector(i * 5.0, 0, 0));

        mobilityModel->SetVelocity(Vector(20, 0, 0));


    // Install WAVE devices

    YansWifiPhyHelper wifiPhy = YansWifiPhyHelper::Default();

    YansWifiChannelHelper wifiChannel = YansWifiChannelHelper::Default();    wifiPhy.SetChannel(wifiChannel.Create());

    QosWaveMacHelper wifiMac = QosWaveMacHelper::Default();

    WaveHelper waveHelper = WaveHelper::Default();

    NetDeviceContainer devices = waveHelper.Install(wifiPhy, wifiMac, vehicles);

    // Install Internet stack

    InternetStackHelper internet;


    // Assign IP addresses

    Ipv4AddressHelper ipv4;

    ipv4.SetBase(“”, “”);

    Ipv4InterfaceContainer interfaces = ipv4.Assign(devices);

    // Set up applications (e.g., OnOffApplication)

    uint16_t port = 9;

    OnOffHelper onOffHelper(“ns3::UdpSocketFactory”, Address(InetSocketAddress(Ipv4Address(“”), port)));    onOffHelper.SetConstantRate(DataRate(“500kb/s”));    onOffHelper.SetAttribute(“OnTime”, StringValue(“ns3::ConstantRandomVariable[Constant=1]”));    onOffHelper.SetAttribute(“OffTime”, StringValue(“ns3::ConstantRandomVariable[Constant=0]”));

    ApplicationContainer apps = onOffHelper.Install(vehicles);



    // Enable packet capture    wifiPhy.EnablePcapAll(“vanet-simulation”);

    // Run the simulation



    return 0;


Step 4: Run the Simulation

  1. Build the Script:
  • In order to demonstrate the script, utilize the waf build system.

./waf build

  1. Run the Simulation:
  • To execute the simulation, we run the compiled script.

./waf –run scratch/vanet-simulation

Step 5: Analyze the Outcomes

  1. PCAP Files:
  • Specifically, to investigate the PCAP files produced at the time of simulation, our team utilizes Wireshark.
  1. NS-3 Traces:
  • NS-3 tracing technologies have to be employed to record and examine data. In our script, it is appreciable to append tracing code.

AsciiTraceHelper ascii;


  1. Visualization:
  • To visualize the simulation, aim to utilize tools such as NetAnim.

./waf –run scratch/vanet-simulation –vis

VANET Simulation Topics

VANET Simulation Topics &Ideas

Over 8000 research scholars have utilized our VANET Simulation services, where we provide them with unique and customized Topics & Ideas that cater to their specific requirements. Our complete range of services includes Problem Identification, complete PhD guidance support, Thesis Writing, Research Analysis, Plagiarism Checking, Proof Reading, and Publication Services. We pride ourselves on being the ultimate solution for all your research needs.

  1. Remora optimization algorithm-based optimized node clustering technique for reliable data delivery in VANETs
  2. Strong key-insulated secure and energy-aware certificateless authentication scheme for VANETs
  3. Survey on Artificial Intelligence (AI) techniques for Vehicular Ad-hoc Networks (VANETs)
  4. PTAP: A novel secure privacy-preserving & traceable authentication protocol in VANETs
  5. A scalable blockchain-based scheme for traffic-related data sharing in VANETs
  6. Understanding The Effect of Physical Parameters on Packet Loss in Veins VANET Simulator
  7. Location privacy in VANETs: Provably secure anonymous key exchange protocol based on self-blindable signatures
  8. FogJam: A Fog Service for Detecting Traffic Congestion in a Continuous Data Stream VANET
  9. Comparative analysis of proactive and reactive routing protocols in VANET environment
  10. A review on specification evaluation of broadcasting routing protocols in VANET
  11. A position-based reliable emergency message routing scheme for road safety in VANETs
  12. ReUse: Reliable and efficient RSU-enabled relay vehicle selection using harmony search and EDAS for message dissemination in VANETs
  13. A comprehensive survey on authentication and privacy-preserving schemes in VANETs
  14. Lightweight Trust Model with Machine Learning scheme for secure privacy in VANET
  15. Blockchain-based secret key extraction for efficient and secure authentication in VANETs
  16. New efficient certificateless scheme-based conditional privacy preservation authentication for applications in VANET
  17. Utilizing VANETs as supplementary communication infrastructure for delay-tolerant bulky data transportation
  18. A certificateless ring signature scheme with batch verification for applications in VANET
  19. Joint power control and multiple antennas optimization for reducing receiver blocking in dense VANETs
  20. BaaS: Broadcast as a service cross-layer learning-based approach in cloud assisted VANETs

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