To simulate a mixed topology in MATLAB has needs to follow a numerous steps that contain a multiple network topologies such as star, ring, mesh, tree in a single network. Mixed topologies are utilized in multifaceted networks to employs the benefits of the diverse structures like integrating a star topology inside the clusters of nodes and associating those clusters with a ring or mesh structure for inter-cluster communication.
The given below are the simple procedures to simulate the mixed topology in MATLAB with an example of star-clustered nodes connected in a ring.
Steps to Simulate a Mixed Topology
- Define the Mixed Network Structure:
- Generate clusters with diverse topologies such as star or bus inside the each group of nodes.
- Associate the clusters using another topology, like a ring or mesh, for inter-cluster communication.
- Simulate Data Transmission:
- Enable the nodes within each cluster to interact via their internal topology.
- Allow an inter-cluster communication via the associated topology like ring.
- Implement Transmission Delays and Routing:
- Estimate transmission latency within clusters and among the clusters in terms of distance and speed.
- For nodes not directly associated, execute a routing mechanism.
- Visualize Network Structure and Transmission Activities:
- Measure the parameters like successful transmissions, inter-cluster delays, and routing paths.
- Utilize MATLAB plots to envision the layout and data flow of the mixed topology.
Example Code for Simulating a Mixed Topology
In this instance, we replicate a mixed topology with three star clusters associated by a ring structure. Each cluster has a central hub which interacts with the other clusters over the ring.
% Parameters for Mixed Topology Simulation
numClusters = 3; % Number of star clusters
nodesPerCluster = 4; % Number of nodes per star cluster, including the hub
totalNodes = numClusters * nodesPerCluster;
distanceWithinCluster = 50; % Distance between nodes in the same cluster
distanceBetweenClusters = 200; % Distance between cluster hubs
propagationSpeed = 2e8; % Propagation speed in meters per second
transmissionProbability = 0.2; % Probability of initiating a transmission at each node per time step
simulationTime = 30; % Duration of the simulation in seconds
% Define Node Positions
clusterPositions = [cos(linspace(0, 2 * pi, numClusters + 1)’) * distanceBetweenClusters, …
sin(linspace(0, 2 * pi, numClusters + 1)’) * distanceBetweenClusters];
clusterPositions = clusterPositions(1:end-1, :); % Remove duplicate last point
% Initialize node positions within each cluster
nodePositions = [];
clusterHubs = [];
for i = 1:numClusters
hubPosition = clusterPositions(i, :);
clusterHubs = [clusterHubs; hubPosition];
for j = 1:nodesPerCluster
if j == 1
nodePositions = [nodePositions; hubPosition]; % Hub position
else
angle = 2 * pi * (j – 1) / (nodesPerCluster – 1);
nodePositions = [nodePositions; hubPosition + distanceWithinCluster * [cos(angle), sin(angle)]];
end
end
end
% Define Connection Matrices
clusterConnection = blkdiag(ones(nodesPerCluster) – eye(nodesPerCluster), …
ones(nodesPerCluster) – eye(nodesPerCluster), …
ones(nodesPerCluster) – eye(nodesPerCluster)); % Star connection in each cluster
% Connect the hubs in a ring structure
hubIndices = 1:nodesPerCluster:totalNodes;
ringConnection = diag(ones(1, numClusters – 1), 1) + diag(ones(1, numClusters – 1), -1);
ringConnection(1, numClusters) = 1; % Connect last hub to first hub
ringConnection(numClusters, 1) = 1;
% Integrate cluster and ring connection into a single connection matrix
connectionMatrix = clusterConnection;
for i = 1:numClusters
for j = i+1:numClusters
connectionMatrix(hubIndices(i), hubIndices(j)) = ringConnection(i, j);
connectionMatrix(hubIndices(j), hubIndices(i)) = ringConnection(j, i);
end
end
% Calculate propagation delays
delayMatrix = (connectionMatrix * distanceWithinCluster) / propagationSpeed;
% Initialize Transmission Log
transmissions = zeros(totalNodes, simulationTime);
% Simulate Data Transmission in Mixed Topology
for t = 1:simulationTime
for node = 1:totalNodes
% Randomly decide if node initiates a transmission
if rand() < transmissionProbability
% Find connected nodes based on topology
connectedNodes = find(connectionMatrix(node, 🙂 == 1);
if ~isempty(connectedNodes)
targetNode = connectedNodes(randi(length(connectedNodes))); % Randomly select a connected node
transmissions(node, t) = 1; % Log transmission
transmissions(targetNode, t + 1) = 1; % Log reception at the next time step
disp([‘Time ‘ num2str(t) ‘s: Node ‘ num2str(node) ‘ transmitted to Node ‘ num2str(targetNode)]);
end
end
end
end
% Visualize Network Layout
figure;
gplot(connectionMatrix, nodePositions, ‘-o’);
hold on;
text(nodePositions(:, 1), nodePositions(:, 2), arrayfun(@num2str, 1:totalNodes, ‘UniformOutput’, false));
title(‘Mixed Topology (Star Clusters Connected in a Ring)’);
xlabel(‘X Position (m)’);
ylabel(‘Y Position (m)’);
axis equal;
hold off;
% Visualize Transmission Activity
time = 1:simulationTime;
figure;
imagesc(transmissions);
colorbar;
title(‘Transmission Activity Over Time in Mixed Topology’);
xlabel(‘Time (s)’);
ylabel(‘Node ID’);
Explanation of the Code
- Parameters:
- numClusters and nodesPerCluster describe the amount of star clusters and nodes in each cluster.
- distanceWithinCluster states the distance among nodes inside the same cluster, since distanceBetweenClusters configures the distance among cluster hubs.
- Connection Matrix:
- clusterConnection describes a star structure inside an each cluster.
- ringConnection associates cluster hubs in a ring, generating the inter-cluster communication channel.
- connectionMatrix integrates clusterConnection and ringConnection into a single adjacency matrix denotes the complete mixed topology.
- Data Transmission Simulation:
- At each time step, each node has a likelihood of beginning the data transmission.
- If a node starts transmission, it chooses an associated node (from connectionMatrix) and transmits the data to it, with the transmission being testified in transmissions.
- Visualization:
- The first plot demonstrating the layout of the mixed topology, showing star clusters associated by a ring.
- The second plot demonstrates transmission activity in excess of time, with each row denotes a node and each column illustrate a time step.
Analysis and Extension Ideas
- Dynamic Traffic Simulation: Modify the transmission probability for each cluster to replicate high-traffic and low-traffic clusters.
- Failure and Redundancy: Mimic the node or link failures, especially in the ring, and reroute data to learn the network’s flexibility.
- Latency and Load Balancing: To measure network efficiency we need to estimate end-to-end latency for intra- and inter-cluster communications
- Prioritized Traffic: Enable the specific nodes or clusters to have to choose the transmission, that helpful for replicating mixed-criticality networks.
- Alternative Inter-Cluster Topology: Exchange the ring connection with a mesh or tree structure for inter-cluster connectivity.
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