How to Deploy Network Intelligent Agents in NS2

To deploy the intelligent agents within NS2 (Network Simulator 2) which require encompasses integrating agent-based systems into the network simulation environment to permit the nodes to create an intelligent decisions according to the real-time conditions like traffic load, energy consumption, or routing efficiency. These agents can be used for tasks such as:

  • Dynamic routing depends on the network conditions.
  • Load balancing by adjusting to traffic changes.
  • Energy management in energy-constrained networks.
  • Network security for intrusion detection and mitigation.

We deliver step-by-step protocol to deploying an intelligent agents in NS2.

Step-by-Step Implementation:

  1. Understand the Role of Intelligent Agents

Intelligent agents within a network simulation can be created to:

  • Observe the network performance such as throughput, delay, packet loss.
  • Create a decisions reply on predefined rules, machine learning models, or optimization algorithms.
  • Performa separately to modify network parameters like routing, load balancing, or energy consumption.
  1. Define the Intelligent Agent Architecture

Before executing the agent in NS2, we describe what the agent will do:

  • Sensing: The agent collects details from the network (e.g., traffic, link quality).
  • Decision-Making: Based on the collected data, the agent makes intelligent decisions such as choosing the best route, balancing the load.
  • Acting: The agent implements actions, like altering routes, modifying transmission power, or moving nodes.
  1. Create Custom Agents in NS2

The simulation NS2 permits to make a custom agents by expanding the Agent class in C++. A custom agent can communicate with other nodes, collect information, and make decisions according on real-time network data.

Example of a Custom Intelligent Agent:

We need to make an agent which observes traffic load and modifies the routing based on network congestion.

Step 1: Define the Intelligent Agent Class in C++

We can make an intelligent agent by expanding the Agent class, describing the essential methods for sensing network conditions, creating decisions, and implementing actions.

Here’s an example:

#include “agent.h”

#include “packet.h”

#include “ip.h”

class IntelligentAgent : public Agent {

public:

IntelligentAgent();

int command(int argc, const char*const* argv); // Command interface

void recv(Packet* p, Handler* h);              // Receiving packets

 

protected:

void monitorNetwork();  // Function to monitor network conditions

void makeDecision();    // Function to make decisions

void act();             // Function to act based on decisions

 

double traffic_load;    // Example parameter the agent tracks

double threshold;       // Load threshold for decision-making

};

// Constructor to initialize the agent

IntelligentAgent::IntelligentAgent() : Agent(PT_UDP), traffic_load(0.0), threshold(20.0) {

// Schedule the network monitoring function

Scheduler::instance().schedule(this, new Event, 1.0);

}

// Command interface for Tcl binding

int IntelligentAgent::command(int argc, const char*const* argv) {

if (argc == 2) {

if (strcmp(argv[1], “start-monitoring”) == 0) {

monitorNetwork();

return TCL_OK;

}

}

return Agent::command(argc, argv);

}

// Monitoring network conditions

void IntelligentAgent::monitorNetwork() {

// Monitor traffic load (can be based on queue length or packet rates)

traffic_load = getTrafficLoad();

// Make a decision if traffic load exceeds threshold

if (traffic_load > threshold) {

makeDecision();

}

// Reschedule the monitoring process

Scheduler::instance().schedule(this, new Event, 1.0);  // Recheck every second

}

// Decision-making based on monitored network conditions

void IntelligentAgent::makeDecision() {

// For example, switch routes or trigger an alternative mechanism

printf(“Traffic load exceeded threshold, adjusting route…\n”);

act();  // Perform the necessary action

}

// Take action based on the decision made

void IntelligentAgent::act() {

// Adjust network parameters (e.g., change routing, load balancing, etc.)

sendNewRouteRequest();

}

Step 2: Compile and Install the Agent

  1. We can save the intelligent agent code in a file like intelligent_agent.cc.
  2. Alter the NS2’s Makefile to contain the new agent.
  3. Recompile NS2 using the make command to construct the intelligent agent.
  1. Use the Intelligent Agent in a Tcl Script

We can use it in a Tcl script, after compiling the custom agent. We will connect the intelligent agent to a node and begin the monitoring process while simulation.

Example Tcl Script:

# Create a simulator instance

set ns [new Simulator]

# Create nodes

set node1 [$ns node]

set node2 [$ns node]

set node3 [$ns node]

# Create a custom intelligent agent and attach it to node1

set agent1 [new Agent/IntelligentAgent]

$ns attach-agent $node1 $agent1

# Start the intelligent agent’s network monitoring process

$ns at 1.0 “$agent1 start-monitoring”

# Create traffic sources (e.g., UDP) and sinks

set udp1 [new Agent/UDP]

set udp2 [new Agent/UDP]

set null [new Agent/Null]

$ns attach-agent $node1 $udp1

$ns attach-agent $node2 $udp2

$ns attach-agent $node3 $null

$ns connect $udp1 $null

$ns connect $udp2 $null

# Define a CBR traffic source to generate traffic

set cbr [new Application/Traffic/CBR]

$cbr set packetSize_ 512

$cbr set interval_ 0.1

$cbr attach-agent $udp1

# Start the simulation

$ns at 1.0 “$cbr start”

$ns run

In this Tcl script:

  • Node 1 is connected to the intelligent agent (Agent/IntelligentAgent).
  • The agent begins observing the network at time 1.0 second.
  • Traffic is produced among the nodes, and the agent reacts while conditions warrant. For instance, if the traffic load exceeds a certain threshold.
  1. Extend the Intelligent Agent with Machine Learning

If we require the intelligent agent to create decisions using machine learning, we can:

  • Train a machine learning model (using Python, TensorFlow, or another ML framework).
  • Interface NS2 with the trained model by calling Python scripts or loading a pre-trained model in C++.

Example:

We can request a Python script from C++ to perform a trained machine learning model and we obtain the decision for route optimization, congestion control, etc.

void IntelligentAgent::makeDecision() {

// Call a Python script to get ML-based decision

system(“python3 ml_decision_script.py”);

// Example of reading the result from the script

std::ifstream result_file(“ml_output.txt”);

std::string decision;

result_file >> decision;

result_file.close();

if (decision == “adjust_route”) {

act();  // Perform the action

}

}

  1. Deploy Multiple Intelligent Agents in the Network

To make a more dispersed or cooperative intelligent system, we can deploy numerous intelligent agents in various nodes over the network. This agents can either manage separately or communicate with each other to distribute the details and enhance the network collectively.

Example Tcl code for deploying multiple intelligent agents:

# Create a simulator

set ns [new Simulator]

# Create nodes

set node1 [$ns node]

set node2 [$ns node]

set node3 [$ns node]

# Create intelligent agents for multiple nodes

set agent1 [new Agent/IntelligentAgent]

set agent2 [new Agent/IntelligentAgent]

# Attach the agents to the nodes

$ns attach-agent $node1 $agent1

$ns attach-agent $node2 $agent2

# Start the agents’ monitoring processes

$ns at 1.0 “$agent1 start-monitoring”

$ns at 1.0 “$agent2 start-monitoring”

# Define traffic sources and sinks as in previous examples

…

# Run the simulation

$ns run

  1. Evaluate Performance

We can assess their effect on the network’s performance, after deploying the intelligent agents using metrics such as:

  • Throughput: Estimate how much data is well transmitted.
  • Latency: Compute the end-to-end delay of packets.
  • Packet delivery ratio: The ratio of packets effectively delivered.
  • Energy efficiency: If we are using energy-aware agents then track the energy consumption.
  • Adaptiveness: Examine how the intelligent agents respond to dynamic network conditions, like congestion or link failures.

Ultimately, we showcase the comprehensive details regarding to deploy and analyse the network Intelligent Agents using the simulation platform NS2. Also we will share further required informations in another manual. You can count on us for timely delivery and comprehensive explanations. Please reach out to us to receive a customized implementation of NFV Communication in the NS2 tool that meets your specific requirements.