How to Start Optimal Routing Projects Using OMNeT++       

To start an Optimal Routing project using OMNeT++ which comprises of modeling or executing the routing algorithms for determining the best paths among the nodes according to a defined optimization criterion like shortest path, minimum latency, maximum throughput, or energy efficiency. Let’s we see how to get started:

Steps to Start Optimal Routing Projects in OMNeT++

  1. Understand Optimal Routing

Key Features

  • Optimization Metrics:
    • Cost: Link delays, hop count, or energy usage.
    • Performance: Increase throughput or reduce latency.
    • Reliability: Prevent the congested or unreliable links.
  • Common Algorithms:
    • Dijkstra’s Algorithm: It supports to find the shortest paths as cost.
    • Bellman-Ford Algorithm: Appropriate for distributed scenarios including negative weights.
    • Linear Programming: For more complex optimization aims like load balancing.

Applications

  • Energy-efficient routing in wireless sensor networks (WSNs).
  • QoS routing for multimedia applications.
  • Traffic engineering within SDN.
  1. Set Up the Environment

Install OMNeT++

  1. We should download and install OMNeT++ environment on the system.
  2. Confirm installation by executing an example simulation.

Install INET Framework

  1. We need to download the INET framework with OMNeT++ version.
  2. Construct the framework:

make makefiles

make

INET framework offers utilities for routing, link modeling, and traffic generation.

  1. Plan the Simulation

Define Objectives

  • Replicate the routing to utilise ideal path computation.
  • Measure the performance parameters like:
    • Resource utilization.
    • Network throughput.
    • End-to-end latency.

Define the Topology

  • Select a network topology such as grid, star, mesh, or random.
  • Allocate the costs or weights to links in terms of delay, bandwidth, or reliability.
  1. Create a New OMNeT++ Project
  1. Go to OMNeT++ IDE.
  2. Select File > New > OMNeT++ Project.
  3. Name it to the project as OptimalRouting and choose Finish.
  1. Implement Optimal Routing Logic

Custom Routing Module

Make a custom routing module using .cc file (e.g., OptimalRouting.cc) for determining and handling the best routes.

Example: Optimal Routing with Dijkstra’s Algorithm

#include <omnetpp.h>

#include <map>

#include <queue>

#include <vector>

using namespace omnetpp;

class OptimalRouting : public cSimpleModule {

private:

struct Link {

int dest;

double cost;

};

std::map<int, std::vector<Link>> networkTopology; // Adjacency list

std::map<int, int> routingTable; // Destination to next-hop mapping

int nodeId;

protected:

virtual void initialize() override {

nodeId = getId();

initializeTopology();

computeOptimalRoutes();

}

void initializeTopology() {

// Example: Initialize network topology (static)

if (nodeId == 0) {

networkTopology[0] = {{1, 1.0}, {2, 4.0}};

networkTopology[1] = {{0, 1.0}, {2, 2.0}, {3, 6.0}};

networkTopology[2] = {{0, 4.0}, {1, 2.0}, {3, 3.0}};

networkTopology[3] = {{1, 6.0}, {2, 3.0}};

}

}

void computeOptimalRoutes() {

// Dijkstra’s Algorithm

std::map<int, double> distances;

std::map<int, int> previous;

std::priority_queue<std::pair<double, int>, std::vector<std::pair<double, int>>, std::greater<>> pq;

// Initialize distances

for (const auto& [node, _] : networkTopology) {

distances[node] = (node == nodeId) ? 0 : INFINITY;

}

pq.push({0, nodeId});

while (!pq.empty()) {

double currentDistance = pq.top().first;

int currentNode = pq.top().second;

pq.pop();

if (currentDistance > distances[currentNode]) continue;

for (const auto& link : networkTopology[currentNode]) {

double newDistance = distances[currentNode] + link.cost;

if (newDistance < distances[link.dest]) {

distances[link.dest] = newDistance;

previous[link.dest] = currentNode;

pq.push({newDistance, link.dest});

}

}

}

// Build routing table

for (const auto& [dest, _] : networkTopology) {

if (dest != nodeId) {

int nextHop = dest;

while (previous[nextHop] != nodeId) {

nextHop = previous[nextHop];

}

routingTable[dest] = nextHop;

}

}

}

virtual void handleMessage(cMessage *msg) override {

auto packet = check_and_cast<cMessage *>(msg);

int dest = packet->par(“dest”);

if (routingTable.find(dest) != routingTable.end()) {

int nextHop = routingTable[dest];

send(packet, “out”, nextHop);

} else {

EV << “No route to destination.\n”;

delete packet;

}

}

};

Define_Module(OptimalRouting);

  1. Define Network Topology

Create a .ned File

Make a network topology including nodes and links:

network OptimalNetwork {

submodules:

node[4]: StandardHost {

parameters:

@display(“i=device/router”);

routingProtocol = “OptimalRouting”;

}

connections:

node[0].pppg++ <–> { delay = 10ms; datarate = 1Gbps; } <–> node[1].pppg++;

node[1].pppg++ <–> { delay = 15ms; datarate = 1Gbps; } <–> node[2].pppg++;

node[2].pppg++ <–> { delay = 5ms; datarate = 1Gbps; } <–> node[3].pppg++;

node[3].pppg++ <–> { delay = 10ms; datarate = 1Gbps; } <–> node[0].pppg++;

}

  1. Configure Simulation Parameters

Edit omnetpp.ini

Define simulation metrics in .ini files:

[Config OptimalRoutingSimulation]

network = OptimalNetwork

**.node[*].routingProtocol = “OptimalRouting”

simulation.timeLimit = 100s

  1. Run the Simulation
  1. Introduce the Tkenv (GUI) or Cmdenv (command line) within OMNeT++ environment.
  2. Monitor:
    • Packet forwarding depends on the ideal routes.
    • Routing table computation.
  1. Analyze Results

Performance Metrics

  • Delivery Ratio: We estimate the rate of packets that are effectively distributed.
  • Path Efficiency: Equate the calculated paths to theoretical ideal paths.
  • Routing Overhead: Compute the cost of route calculation.

Visualization

  • Observe the packet forwarding and routing decisions to utilise OMNeT++’s tools for visualization.
  • Transfer records into external tools such as MATLAB, Python, or Excel for detailed analysis.
  1. Extend and Optimize

Dynamic Scenarios

  • Launch link/node failures to experiment the robustness of the routing algorithm.
  • For dynamic topology updates, mimic mobility.

Multi-Criteria Optimization

  • Make use of several performance parameters like latency and bandwidth for routing decisions.
  • We need to execute the linear programming or heuristic algorithms for more complex goals.

Comparison

  • We have to equate the optimal routing including non-optimal strategies like flooding or random routing.

In this manual, you can discover more insights and structured simulation procedure for Optimal Routing projects, which was implemented and replicated using OMNeT++ environment. Should there be more questions on this topic, we can address in another manual.

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