How to Implement Deep Learning Routing in NS2

To implement the deep learning-based routing within NS2 (Network Simulator 2) that has requires to encompass incorporating the machine learning methods including the existing routing mechanisms to enhance routing decisions actively rely on the real-time network conditions. These models like neural networks can guess optimal paths, reduce latency, enhance the packet delivery, and manage difficult network scenarios such as high mobility, varying traffic loads, and dynamic topologies. Because the simulation NS2 is not create to directly support deep learning, the key challenge is incorporating NS2 including a deep learning framework like TensorFlow or PyTorch. It normally contains using a combination of external Python scripts for deep learning and C++/Tcl code in the simulation environment NS2 for packet managing and simulation.

Step-by-Step Guide to Implementing Deep Learning Routing in NS2

  1. Identify the Routing Objectives for Deep Learning

Before executing the deep learning routing algorithm, we want to describe the objectives:

  • Predict optimal paths according to the traffic conditions, link quality, or mobility patterns.
  • Enhance routing performance by reducing delay, maximizing throughput, or make sure energy efficiency in wireless sensor networks.
  • Manage dynamic changes in the network, like link failures or congestion.
  1. Create a Deep Learning Model for Routing

These deep learning model will know to guess the finest routes depending on the input features like node positions, link quality, bandwidth, and traffic load. The training process will normally occur the offline using data gathered from the network simulation or real-world deployments.

Example Deep Learning Model (Using TensorFlow or PyTorch):

We can use any deep learning framework to make a model that predicts the next hop or optimal route according to the input features.

Given below an instance using TensorFlow to make a basic feedforward neural network for routing decisions:

import tensorflow as tf

from tensorflow.keras import layers

# Define the input shape (for example, the features could be node positions, traffic load, etc.)

input_shape = (num_features,)

# Create a simple feedforward neural network

model = tf.keras.Sequential([

layers.InputLayer(input_shape=input_shape),

layers.Dense(64, activation=’relu’),

layers.Dense(32, activation=’relu’),

layers.Dense(num_nodes, activation=’softmax’)  # Output represents probabilities for each next hop

])

# Compile the model

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

# Train the model using historical data from the network (offline training)

# X_train and y_train represent the input features and corresponding next hop labels

model.fit(X_train, y_train, epochs=10)

# Save the trained model

model.save(‘routing_model.h5’)

These model predicts the finest next hop depending on the network conditions in which the output layer denotes the probabilities of selecting each possible next hop.

  1. Integrate the Deep Learning Model with NS2

Because the simulation environment NS2 is written in C++ and Tcl that the next step is to incorporate the deep learning model with NS2. It can be completed by calling Python scripts (which use TensorFlow/PyTorch) from the NS2 to create routing decisions rely on the real-time network data.

3.1 Prepare Python Script for Inference

When the deep learning model is trained and saved, we can make a Python script which loads the model and uses it to create the routing predictions.

import tensorflow as tf

import numpy as np

# Load the pre-trained model

model = tf.keras.models.load_model(‘routing_model.h5’)

# Function to predict the next hop based on input features (e.g., link quality, node positions, etc.)

def predict_next_hop(features):

# Reshape the input features for the model

features = np.array(features).reshape(1, -1)

# Predict the next hop probabilities

predictions = model.predict(features)

# Get the next hop with the highest probability

next_hop = np.argmax(predictions)

return next_hop

# Example input features (this would come from NS2 during the simulation)

features = [0.5, 0.3, 0.8, 0.6]  # Example feature values representing link quality, etc.

print(“Predicted next hop:”, predict_next_hop(features))

3.2 Modify the Routing Protocol in NS2 (C++)

In NS2, we will change or make a new routing protocol which requests the Python script to decide the next hop. We can use system calls from C++ to communicate with the Python script.

Example C++ Code to Call Python Script for Routing Decisions:

#include <iostream>

#include <cstdlib>

#include <cstdio>

class DeepLearningRoutingAgent : public Agent {

public:

DeepLearningRoutingAgent();

void recv(Packet* p);  // Receive packets and make routing decisions

protected:

int predictNextHop(const std::vector<double>& features);  // Predict the next hop using the Python script

};

// Constructor

DeepLearningRoutingAgent::DeepLearningRoutingAgent() : Agent(PT_UDP) {}

// Function to predict the next hop by calling the Python script

int DeepLearningRoutingAgent::predictNextHop(const std::vector<double>& features) {

// Prepare the feature input as a string

std::string featureStr;

for (double feature : features) {

featureStr += std::to_string(feature) + ” “;

}

// Call the Python script and pass the features as arguments

std::string command = “python3 predict_next_hop.py ” + featureStr;

FILE* pipe = popen(command.c_str(), “r”);

if (!pipe) return -1;

char buffer[128];

std::string result = “”;

while (fgets(buffer, 128, pipe) != nullptr) {

result += buffer;

}

pclose(pipe);

// Convert the result to an integer (next hop)

return std::stoi(result);

}

// Override the recv function to decide the next hop using deep learning

void DeepLearningRoutingAgent::recv(Packet* p) {

hdr_ip* iph = HDR_IP(p);

// Collect the necessary features (e.g., link quality, traffic load, etc.)

std::vector<double> features = {0.5, 0.8};  // Example features (this should be based on network state)

// Predict the next hop using the deep learning model

int nextHop = predictNextHop(features);

// Forward the packet to the predicted next hop

if (nextHop != -1) {

sendto(p, nextHop);

} else {

Packet::free(p);  // Drop the packet if no valid route is found

}

}

In this example:

  • The DeepLearningRoutingAgent class gets the packets and also using a deep learning model (via a Python script) predicts the next hop.
  • The predictNextHop function requires the Python script (predict_next_hop.py), passing the essential network features as arguments. The Python script then returns the predicted next hop.
  1. Simulate Deep Learning-Based Routing in NS2

When the deep learning model is incorporated into the simulation NS2, we can replicate the routing protocol using Tcl scripts. The simulation will gather the real-time network data (like node positions, link quality, etc.) then we use the trained deep learning model to create a routing decisions.

Example Tcl Script for Deep Learning Routing Simulation:

# Create a simulator instance

set ns [new Simulator]

# Create nodes

set node1 [$ns node]

set node2 [$ns node]

set node3 [$ns node]

# Attach the deep learning routing agent to nodes

set dlAgent1 [new Agent/DeepLearningRoutingAgent]

set dlAgent2 [new Agent/DeepLearningRoutingAgent]

$ns attach-agent $node1 $dlAgent1

$ns attach-agent $node2 $dlAgent2

# Define traffic between nodes

set udp1 [new Agent/UDP]

$ns attach-agent $node1 $udp1

$ns connect $udp1 $dlAgent2

set cbr [new Application/Traffic/CBR]

$cbr set packetSize_ 512

$cbr set interval_ 0.05

$cbr attach-agent $udp1

# Start traffic

$ns at 1.0 “$cbr start”

# Run the simulation

$ns run

  1. Evaluate the Performance

We can assess the performance of the deep learning-based routing algorithm, after running the simulation, using the following metrics:

  • Packet Delivery Ratio (PDR): Calculate the percentage of effectively delivered packets.
  • Throughput: Examine the network throughput depends on the deep learning routing decisions.
  • Latency: Estimate the end-to-end delay experienced by packets.
  • Adaptability: Verify how successfully the deep learning model adjusts to dynamic changes in the network (e.g., node mobility, link failures).
  • Energy Efficiency: In wireless sensor networks, we observe the energy consumption to make sure that deep learning routing doesn’t utilize excessive energy.

We provided complete essential details and approaches on how to implement and examine the Deep Learning Routing the simulation environment NS2. We will offer more insights regarding this topic in another manual.

For a successful setup of Deep Learning Routing in NS2,  ns2project.com will be your trusted partner. Our skilled developers provide great support. Get performance advice for your project from our experts. Our team specializes in frameworks like TensorFlow and PyTorch based on your project needs.