How to Implement Artificial Intelligence for Networks in ns3

To implement the artificial intelligence (AI) for networks in NS3 it is carried on by us by  combining the machine learning techniques to enhance the network performance, automate decision making developments, and optimize the adaptability of network protocols under different conditions. This is usually completed by modelling network environment in ns3 and then using the external machine learning approaches or libraries such as pythons Tensor flow or Pytorch to evaluate the data and make decisions. Implementation of AI for networks in NS3  with coding based on your projects are worked by us.

The given below are the step-by-step procedures on how to integrate the AI concepts with the network simulation in ns-3 environment:

Step-by-Step Guide to Implement Artificial Intelligence for Networks in ns-3

1. Install and Set Up NS3

To make sure NS-3 is installed on your system.  Make sure to have a working development environment with Python, as it facilitates integration with machine learning libraries.

2. Define the Network Scenario

Create a basic network scenario in NS3 that you want to optimize or study using AI. For example, set up a simple network topology with multiple nodes and a varying traffic pattern.

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

#include “ns3/point-to-point-module.h”

#include “ns3/applications-module.h”

using namespace ns3;

int main () {

    NodeContainer nodes;

    nodes.Create(2);

    PointToPointHelper pointToPoint;

    pointToPoint.SetDeviceAttribute(“DataRate”, StringValue(“5Mbps”));

    pointToPoint.SetChannelAttribute(“Delay”, StringValue(“2ms”));

    NetDeviceContainer devices;

    devices = pointToPoint.Install(nodes);

    InternetStackHelper stack;

    stack.Install(nodes);

    Ipv4AddressHelper address;

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

    Ipv4InterfaceContainer interfaces = address.Assign(devices);

    UdpEchoServerHelper echoServer(9);

    ApplicationContainer serverApps = echoServer.Install(nodes.Get(1));

    serverApps.Start(Seconds(1.0));

    serverApps.Stop(Seconds(10.0));

    UdpEchoClientHelper echoClient(interfaces.GetAddress(1), 9);

    echoClient.SetAttribute(“MaxPackets”, UintegerValue(10));

    echoClient.SetAttribute(“Interval”, TimeValue(Seconds(1.0)));

    echoClient.SetAttribute(“PacketSize”, UintegerValue(1024));

    ApplicationContainer clientApps = echoClient.Install(nodes.Get(0));

    clientApps.Start(Seconds(2.0));

    clientApps.Stop(Seconds(10.0));

    Simulator::Run();

    Simulator::Destroy();

    return 0;

}

3. Collect Data

Modify your simulation to log relevant network performance metrics such as throughput, delay, packet loss, etc. This data will be used to train your machine learning model.

4. Integrate Machine Learning Models

• Use a Python script to process the simulation output data for training an AI model. You can utilize libraries like TensorFlow, PyTorch, or Scikit-learn for this purpose.
• Train a model to predict network behavior or to make decisions (e.g., for dynamic routing, congestion control strategies).

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

# Example: Load and preprocess data

data = pd.read_csv(‘network_data.csv’)

X = data.drop(‘target’, axis=1)

y = data[‘target’]

# Split data

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a simple model

model = RandomForestClassifier()

model.fit(X_train, y_train)

# Predict

predictions = model.predict(X_test)

# Evaluate the model

from sklearn.metrics import accuracy_score

print(“Accuracy:”, accuracy_score(y_test, predictions))

5. Apply AI Decisions Back to NS3

Based on the predictions or decisions made by your AI model, adjust the network configurations in your NS3 simulation to study improved outcomes. This might involve dynamically changing routing protocols, adjusting bandwidth allocations, or simulating traffic control mechanisms based on the model’s output.

6. Iterate and Refine

Continuously refine your machine learning models and NS3 simulations based on the outcomes and discoveries from each simulation run. Use new data to improve the models accuracy and the network’s performance.

Finally, we discussed here about how to integrate the AI concepts in ns-3 environment detailed and also we provide simulation results based on artificial intelligence network project..