How to Implement Network Intelligent Agents deployed in ns3

To implement the network intelligent agents in ns3 has embrace to incorporate the machine learning or artificial intelligence techniques that enthusiastically adjust and enhance the network performance based on the real-time.

The developers at ns3simulation.com will carry out implementing Network Intelligent Agents in ns3programming. Our team conducts performance analytics for your research projects to ensure the best possible outcome.

In the below we are providing the elaborate guide to implement the network intelligence agents in ns3:

Step-by-Step Implementation:

Step 1: Set Up the ns3 Environment

1. Install ns3: Make certain ns3 is installed in the system.

sudo apt-get update

sudo apt-get install ns3

Create a New ns3 Project: Create a directory for your new project within the ns3 workspace.

cd ns-3

mkdir scratch/intelligent-agents-project

Step 2: Integrate AI Libraries

1. Choose an AI Library: Select an AI library such as TensorFlow, PyTorch, or Scikit-learn. Ensure it is compatible with C++ or Python, as ns3 supports both languages.
2. Install the AI Library: Install the AI library using pip for Python or appropriate installation methods for C++.

pip install tensorflow

1. Link the AI Library with ns3: For Python, create bindings between ns3 and your AI library. For C++, link the library in your wscript or txt.

Step 3: Develop the Intelligent Agent

1. Define the AI Model: Create an AI model for the intelligent agent. This model could be a neural network, a reinforcement learning agent, or any other suitable model.

For example, a simple neural network in TensorFlow:

import tensorflow as tf

from tensorflow.keras import layers

model = tf.keras.Sequential([

layers.Dense(64, activation=’relu’, input_shape=(input_dim,)),

layers.Dense(64, activation=’relu’),

layers.Dense(output_dim, activation=’softmax’)

])

model.compile(optimizer=’adam’,loss=’sparse_categorical_crossentropy’,metrics=[‘accuracy’])

Train the Model: Train your model using historical data or simulated data. Guarantee that the training process includes relevant features for network optimization.

model.fit(training_data, training_labels, epochs=10, validation_data=(validation_data, validation_labels))

Step 4: Implement the Intelligent Agent in ns3

1. Create a New ns3 Script: Create a new script in your scratch directory to implement the simulation scenario.

// intelligent-agents-project.cc

#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”

#include “ns3/mobility-module.h”

#include “ns3/aodv-module.h”

#include “tensorflow/core/public/session.h”

#include “tensorflow/core/platform/env.h”

using namespace ns3;

using namespace tensorflow;

void IntelligentAgentDecision(Ptr<Node> node) {

// Load the trained AI model

// Predict actions based on current network state

// Apply the actions to optimize the network

}

void ScheduleAgentDecisions(NodeContainer nodes) {

for (uint32_t i = 0; i < nodes.GetN(); ++i) {

Simulator::Schedule(Seconds(10.0), &IntelligentAgentDecision, nodes.Get(i));

}

}

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

CommandLine cmd;

cmd.Parse(argc, argv);

NodeContainer nodes;

nodes.Create(4);

PointToPointHelper pointToPoint;

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

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

NetDeviceContainer devices;

devices = pointToPoint.Install(nodes);

InternetStackHelper stack;

AodvHelper aodv;

stack.SetRoutingHelper(aodv);

stack.Install(nodes);

Ipv4AddressHelper address;

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

Ipv4InterfaceContainer interfaces = address.Assign(devices);

// Set up mobility

MobilityHelper mobility;

mobility.SetMobilityModel(“ns3::ConstantPositionMobilityModel”);

mobility.Install(nodes);

// Schedule intelligent agent decisions

ScheduleAgentDecisions(nodes);

// Create traffic source and sink

UdpEchoServerHelper echoServer(9);

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

serverApps.Start(Seconds(1.0));

serverApps.Stop(Seconds(20.0));

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

echoClient.SetAttribute(“MaxPackets”, UintegerValue(100));

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(20.0));

// Enable tracing

AsciiTraceHelper ascii;

pointToPoint.EnableAsciiAll(ascii.CreateFileStream(“intelligent-agents.tr”));

pointToPoint.EnablePcapAll(“intelligent-agents”);

Simulator::Stop(Seconds(20.0));

Simulator::Run();

Simulator::Destroy();

return 0;

}

1. Implement the Intelligent Agent Decision Function: Implement the IntelligentAgentDecision function to load the trained AI model and use it to make decisions based on the current network state.

Step 5: Run and Validate the Simulation

1. Build the Project: Build your ns3 project to ensure that everything is linked correctly.

./waf build

Run the Simulation: Run your simulation script and observe the results.

./waf –run scratch/intelligent-agents-project

Validate and Analyse Results: Validate the performance of the intelligent agent by analysing the simulation results. Compare them with traditional methods to evaluate improvements.

In the end, we learn and get knowledge about how network intelligent agents integrate the machine learning and AI techniques in real time network to optimize the performance using the ns3 implementation tool. Additional details concerning the implementation of the network intelligent agents in diverse simulations will also be provided.