To implement deep learning-based routing in ns3, we need to follow several steps. First, we have to create a routing protocol to make routing decisions by using deep learning models. The following steps will guide on how to implement Deep Learning based routing in ns3.
Step-by-step guide to implement this routing in ns3
Step 1: Set Up the ns3 Environment
- Install ns-3: Make sure that ns3 is installed on the system.
sudo apt-get update
sudo apt-get install ns3
Create a New ns3 Project: Create a directory for the new project within the ns-3 workspace.
cd ns-3
mkdir scratch/deep-learning-routing
Step 2: Integrate Deep Learning Library
- Install PyTorch: Install a deep learning library like PyTorch. Ensure that Python and pip installed on the system.
pip install torch
Step 3: Define the Deep Learning Model
- Create a Deep Learning Model: Create a Python script that defines the deep learning model. For simplicity, we’ll create a basic feedforward neural network.
# deep_learning_model.py
import torch
import torch.nn as nn
import torch.optim as optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(10, 50)
self.fc2 = nn.Linear(50, 50)
self.fc3 = nn.Linear(50, 2) # Output two values for routing decision
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
def train_model():
net = Net()
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)
# Dummy training data
inputs = torch.randn(100, 10)
targets = torch.randn(100, 2)
for epoch in range(1000): # loop over the dataset multiple times
optimizer.zero_grad() # zero the parameter gradients
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
torch.save(net.state_dict(), “deep_model.pth”)
print(“Finished Training”)
if __name__ == “__main__”:
train_model()
Train the model:
python deep_learning_model.py
Step 4: Integrate the Model with ns3
- Create the Routing Protocol Class: Define a new routing protocol class that uses the trained deep learning model for routing decisions.
// DeepLearningRouting.h
#ifndef DEEP_LEARNING_ROUTING_H
#define DEEP_LEARNING_ROUTING_H
#include “ns3/ipv4-routing-protocol.h”
#include “ns3/ipv4.h”
#include “ns3/ipv4-routing-table-entry.h”
#include “ns3/timer.h”
#include <map>
#include <vector>
#include <torch/script.h>
namespace ns3 {
class DeepLearningRouting : public Ipv4RoutingProtocol {
public:
static TypeId GetTypeId (void);
DeepLearningRouting ();
virtual ~DeepLearningRouting ();
virtual Ptr<Ipv4Route> RouteOutput (Ptr<Packet> p, const Ipv4Header &header, Ptr<NetDevice> oif, Socket::SocketErrno &sockerr);
virtual bool RouteInput (Ptr<const Packet> p, const Ipv4Header &header, Ptr<const NetDevice> idev, UnicastForwardCallback ucb, MulticastForwardCallback mcb, LocalDeliverCallback lcb, ErrorCallback ecb);
virtual void NotifyInterfaceUp (uint32_t interface);
virtual void NotifyInterfaceDown (uint32_t interface);
virtual void NotifyAddAddress (uint32_t interface, Ipv4InterfaceAddress address);
virtual void NotifyRemoveAddress (uint32_t interface, Ipv4InterfaceAddress address);
virtual void SetIpv4 (Ptr<Ipv4> ipv4);
virtual void PrintRoutingTable (Ptr<OutputStreamWrapper> stream, Time::Unit unit = Time::S) const;
private:
torch::jit::script::Module m_model;
Ptr<Ipv4> m_ipv4;
std::map<Ipv4Address, Ipv4RoutingTableEntry> m_routes;
Timer m_timer;
};
} // namespace ns3
#endif // DEEP_LEARNING_ROUTING_H
// DeepLearningRouting.cc
#include “DeepLearningRouting.h”
#include “ns3/log.h”
#include “ns3/ipv4-l3-protocol.h”
namespace ns3 {
NS_LOG_COMPONENT_DEFINE (“DeepLearningRouting”);
NS_OBJECT_ENSURE_REGISTERED (DeepLearningRouting);
TypeId DeepLearningRouting::GetTypeId (void) {
static TypeId tid = TypeId (“ns3::DeepLearningRouting”)
.SetParent<Ipv4RoutingProtocol> ()
.SetGroupName (“Internet”)
.AddConstructor<DeepLearningRouting> ();
return tid;
}
DeepLearningRouting::DeepLearningRouting () {
m_model = torch::jit::load(“deep_model.pth”);
}
DeepLearningRouting::~DeepLearningRouting () {}
void DeepLearningRouting::SetIpv4 (Ptr<Ipv4> ipv4) {
m_ipv4 = ipv4;
}
void DeepLearningRouting::NotifyInterfaceUp (uint32_t interface) {}
void DeepLearningRouting::NotifyInterfaceDown (uint32_t interface) {}
void DeepLearningRouting::NotifyAddAddress (uint32_t interface, Ipv4InterfaceAddress address) {}
void DeepLearningRouting::NotifyRemoveAddress (uint32_t interface, Ipv4InterfaceAddress address) {}
Ptr<Ipv4Route> DeepLearningRouting::RouteOutput (Ptr<Packet> p, const Ipv4Header &header, Ptr<NetDevice> oif, Socket::SocketErrno &sockerr) {
// Implement route output logic here using the deep learning model
return 0;
}
bool DeepLearningRouting::RouteInput (Ptr<const Packet> p, const Ipv4Header &header, Ptr<const NetDevice> idev, UnicastForwardCallback ucb, MulticastForwardCallback mcb, LocalDeliverCallback lcb, ErrorCallback ecb) {
// Implement route input logic here using the deep learning model
return false;
}
void DeepLearningRouting::PrintRoutingTable (Ptr<OutputStreamWrapper> stream, Time::Unit unit) const {
// Implement routing table printing here
}
} // namespace ns3
Step 5: Integrate the Routing Protocol in a Simulation
- Create a New Simulation Script: Create a new script in the scratch directory to use the deep learning-based routing protocol.
// deep-learning-routing.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 “DeepLearningRouting.h”
using namespace ns3;
NS_LOG_COMPONENT_DEFINE (“DeepLearningRoutingExample”);
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;
Ptr<DeepLearningRouting> deepRouting = CreateObject<DeepLearningRouting> ();
stack.SetRoutingHelper (deepRouting);
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 (3));
serverApps.Start (Seconds (1.0));
serverApps.Stop (Seconds (10.0));
UdpEchoClientHelper echoClient (interfaces.GetAddress (3), 9);
echoClient.SetAttribute (“MaxPackets”, UintegerValue (1));
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;
}
Step 6: Compile and Run the Simulation
- Compile the Script: Compile the script using the waf build system.
./waf build
Run the Simulation: Run the simulation script and observe the results.
./waf –run scratch/deep-learning-routing
Step 7: Enable Tracing and Analyze Results
- Enable Tracing: Add tracing to collect data for analysis.
AsciiTraceHelper ascii;
pointToPoint.EnableAsciiAll (ascii.CreateFileStream (“deep-learning-routing.tr”));
Run the Simulation: Set the simulation stop time and run it.
Simulator::Stop (Seconds (10.0));
Simulator::Run ();
Simulator::Destroy ();
Step 8: Analyze the Results
After running the simulation, analyze the generated trace files (deep-learning-routing.tr) to study the routing behavior and performance.
Finally, we had implemented the Deep Learning based routing in ns3 by creating a routing protocol which utilizes the deep learning models to make routing decisions. After the configuration we have to enable the tracing for anlyzing the results.
A variety of project ideas with comparative analysis focused on Deep Learning-based Routing in the ns3 tool are available for students. For optimal outcomes, visit ns3simulation.com.