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.

2. 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.

2. 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.

2. Simulation Setup

• This section encompasses the explanation of arrangement and tools.
• Metrics and settings.

3. Performance Metrics

• Generally, explanations and significance of every parameter are involved.

4. Results and Analysis

• Our team includes statistical analysis and visualization of outcomes.
• Comparison of settings/protocols.

5. Discussion

• Explanation of outcomes.
• Perceptions and impacts are encompassed in this segment.

6. 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.

2. End-to-End Delay:

• DSR has higher delay in high-density but less delay in low-density settings.
• Among loads, GPSR sustains coherent delay.

3. 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.

4. 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

wget https://www.nsnam.org/release/ns-allinone-3.33.tar.bz2

tar xjf ns-allinone-3.33.tar.bz2

cd ns-allinone-3.33

./build.py –enable-examples –enable-tests

2. 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.

2. 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.

3. 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., vanet-simulation.cc).

2. 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;

3. 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”);

    mobility.Install(vehicles);

    // 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;

    internet.Install(vehicles);

    // Assign IP addresses

    Ipv4AddressHelper ipv4;

    ipv4.SetBase(“10.1.1.0”, “255.255.255.0”);

    Ipv4InterfaceContainer interfaces = ipv4.Assign(devices);

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

    uint16_t port = 9;

    OnOffHelper onOffHelper(“ns3::UdpSocketFactory”, Address(InetSocketAddress(Ipv4Address(“10.1.1.255”), 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);

    apps.Start(Seconds(1.0));

    apps.Stop(Seconds(10.0));

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

    // Run the simulation

    Simulator::Run();

    Simulator::Destroy();

    return 0;

}

Step 4: Run the Simulation

1. Build the Script:

• In order to demonstrate the script, utilize the waf build system.

./waf build

2. 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.

2. 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;

wifiPhy.EnableAsciiAll(ascii.CreateFileStream(“vanet-simulation.tr”));

3. Visualization:

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

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