To calculate the number of clusters in the OMNeT++, we need to simulate a network which specifies the group of nodes that are more deeply linked to one another than to the rest of the network. For efficient communication and energy management, we can use clustering which is typically used in wireless sensor networks (WSNs) and mobile ad-hoc networks (MANETs). We provided the step-by-step approach to calculate the number of cluster in OMNeT++:

**Step-by-Step Implementation:**

**Define What Constitutes a Cluster**

A cluster is usually stated as a group of nodes that are more closely connected to each other than to nodes outside the cluster. In the context of networking, clusters might be defined by:

**Proximity**: Nodes that are geographically close to each other.**Connectivity**: Nodes that have straight communication links with each other.**Cluster Head**: A designated node that handles communication inside the cluster.

**Implement a Clustering Algorithm**

We need to execute a clustering algorithm or use an existing one to classify clusters. Common clustering algorithms include:

**K-means**: Nodes are gathered as per the proximity to a set of central points.**Hierarchical Clustering**: Nodes are recursively grouped into clusters.**LEACH (Low-Energy Adaptive Clustering Hierarchy)**: A popular algorithm in WSNs where nodes elect cluster heads.

For simplicity, I’ll show how to calculate the number of clusters by pretentious that each node knows its cluster ID, which could be allotted using one of these algorithms.

**Track Cluster IDs**

Every node should have an allocated cluster ID that specifies the cluster to which it belongs.

**Example: Assigning and Storing Cluster IDs**

class Node : public cSimpleModule {

private:

int clusterId;

protected:

virtual void initialize() override {

// Example: Assign cluster IDs based on predefined logic

// This could be part of a more complex clustering algorithm

clusterId = intuniform(0, 3); // Randomly assign one of 4 clusters

EV << “Node assigned to cluster ID: ” << clusterId << endl;

recordScalar(“Cluster ID”, clusterId);

}

int getClusterId() {

return clusterId;

}

};

**Calculate the Number of Clusters**

We have to loop over all nodes, aggregate their cluster IDs and count the unique IDs to estimate the total number of clusters.

**Example: Counting Unique Cluster IDs**

std::set<int> uniqueClusterIds;

void countClusters() {

int numNodes = getParentModule()->getSubmoduleVectorSize(“node”);

for (int i = 0; i < numNodes; i++) {

cModule *node = getParentModule()->getSubmodule(“node”, i);

Node *nodeModule = check_and_cast<Node *>(node);

uniqueClusterIds.insert(nodeModule->getClusterId());

}

EV << “Number of clusters: ” << uniqueClusterIds.size() << endl;

recordScalar(“Number of Clusters”, uniqueClusterIds.size());

}

**Record the Number of Clusters**

After we calculated the number of clusters, we can record this value for analysis.

void finish() override {

countClusters();

}

**Run the Simulation**

Based on the clustering logic implemented, we have to calculate the number of clusters by implanting the simulation in OMNeT++.

**Analyze the Results**

Later the simulation, you can analyze the recorded number of clusters to understand how the nodes are grouped within the network.

**Example Scenario**

Follow the complete example of how to calculate the number of clusters in an OMNeT++ simulation:

class Node : public cSimpleModule {

private:

int clusterId;

protected:

virtual void initialize() override {

clusterId = intuniform(0, 3); // Randomly assign one of 4 clusters

EV << “Node assigned to cluster ID: ” << clusterId << endl;

recordScalar(“Cluster ID”, clusterId);

}

int getClusterId() {

return clusterId;

}

};

class Network : public cSimpleModule {

private:

std::set<int> uniqueClusterIds;

protected:

virtual void finish() override {

countClusters();

}

void countClusters() {

int numNodes = getSubmoduleVectorSize(“node”);

for (int i = 0; i < numNodes; i++) {

cModule *node = getSubmodule(“node”, i);

Node *nodeModule = check_and_cast<Node *>(node);

uniqueClusterIds.insert(nodeModule->getClusterId());

}

EV << “Number of clusters: ” << uniqueClusterIds.size() << endl;

recordScalar(“Number of Clusters”, uniqueClusterIds.size());

}

};

**Post-Simulation Analysis**

After running the simulation, inspect the number of clusters using OMNeT++’s analysis tools. This analysis can help you understand the efficiency of the clustering algorithm and how well the network is organized.

**Additional Considerations**

**Cluster Size**: Count the number of nodes inside each cluster to calculate the size of each cluster.**Dynamic Clustering**: Clusters might differ over time in dynamic networks, so we may need to calculate the number of clusters at various time points through the simulation.

In this process, we had covered the details from the simulation set up to post processing the results of the Network Number of Clusters in OMNeT++’s Calculation. For further analysis or requirements, we will help you with additional information of this process.

We’re ready to help you understand how your network is doing using the Network Number of clusters feature in the omnet++ program. With insights from an experienced developer, we can provide you with clear explanations and some cool project ideas to explore.