How to Simulate Spanning Tree Protocol Projects Using MATLAB

To simulate the Spanning Tree Protocol (STP) in MATLAB, we can design a network with multiple switches (nodes) and links, in which STP is utilized to mitigate loops by generating a spanning tree which contain all switches with the least amount of links. In STP, a root switch is chosen, and all other switches regulate the shortest path to the root, hindering redundant paths to make sure a loop-free topology.

Here’s a structured approach to simulating STP in MATLAB.

Steps to Simulate STP in MATLAB

1. Define Network Topology:
   • Utilize an adjacency matrix or graph object to signify switches (nodes) and links.
   • Allocate weights to every link to signify the cost that could be established on link speed, delay, or other parameters.
2. Root Bridge Election:
   • Choose the switch with the lowest ID by way of the root bridge (for simplicity, utilize the lowest-numbered node).
   • Every other switch will identify their shortest path to this root bridge.
3. Compute Shortest Paths to the Root Bridge:
   • Utilize Dijkstra’s algorithm to estimate the shortest path from each switch to the root bridge.
   • Spot only the links which create part of these shortest paths, blocking all other links.
4. Simulate Spanning Tree Formation:
   • Build a spanning tree which contain all switches and eliminate any redundant paths, making sure a loop-free topology.
5. Visualize and Analyze Results:
   • Plot the network topology, emphasizing active links in the spanning tree.
   • Measure the parameters such as path cost, hop count, and make sure there are no loops.

Example Code Outline

Here’s an instance MATLAB code summary to replicate STP with root bridge election and spanning tree formation.

% Define network topology as an adjacency matrix with link weights

adjMatrix = [

0 2 0 0 7;

2 0 3 8 0;

0 3 0 1 6;

0 8 1 0 4;

7 0 6 4 0

];

numNodes = size(adjMatrix, 1);

% Select the root bridge (node with the lowest ID for simplicity)

rootBridge = 1; % You can also choose a different node by ID

disp([‘Root Bridge elected: Node ‘, num2str(rootBridge)]);

% Function to perform Dijkstra’s algorithm for finding shortest path from the root bridge

function [dist, prev] = dijkstra(adjMatrix, source)

numNodes = size(adjMatrix, 1);

dist = inf(1, numNodes);  % Initialize distances to infinity

dist(source) = 0;          % Distance to itself is 0

visited = false(1, numNodes);

prev = NaN(1, numNodes);   % Previous node in optimal path

for i = 1:numNodes

% Find the unvisited node with the smallest distance

[~, u] = min(dist + visited * inf);

visited(u) = true;

% Update distances to neighboring nodes

for v = 1:numNodes

if adjMatrix(u, v) > 0 && ~visited(v)  % Check for edge and if unvisited

alt = dist(u) + adjMatrix(u, v);

if alt < dist(v)

dist(v) = alt;

prev(v) = u;

end

end

end

end

end

% Compute shortest paths to the root bridge

[dist, prev] = dijkstra(adjMatrix, rootBridge);

% Construct the spanning tree based on the shortest paths

spanningTree = zeros(numNodes); % Initialize an empty matrix for the spanning tree

for node = 1:numNodes

if node ~= rootBridge && ~isnan(prev(node))

spanningTree(node, prev(node)) = adjMatrix(node, prev(node));

spanningTree(prev(node), node) = adjMatrix(node, prev(node)); % Make it bidirectional

end

end

% Display the spanning tree

disp(‘Spanning Tree Matrix:’);

disp(spanningTree);

% Visualize the original network topology and the resulting spanning tree

G = graph(adjMatrix);

figure;

subplot(1, 2, 1);

plot(G, ‘EdgeLabel’, G.Edges.Weight);

title(‘Original Network Topology’);

% Plot the spanning tree

T = graph(spanningTree);

subplot(1, 2, 2);

plot(T, ‘EdgeLabel’, T.Edges.Weight);

title(‘Spanning Tree Topology’);

Explanation of the Code

• Network Topology: The adjacency matrix adjMatrix describes the network in which each non-zero entry signifies a link and its cost (weight).
• Root Bridge Election: For ease, the node with the lowest ID is selected by the way of the root bridge.
• Dijkstra’s Algorithm for Shortest Path Calculation: The dijkstra function estimates the shortest path from the root bridge to all other nodes, according to link costs.
• Spanning Tree Construction: The spanningTree matrix records the links which form the spanning tree by associating each node to its previous node (closest to the root bridge).
• Visualization: The original network and the resultant spanning tree are showed side-by-side, with edges noticeable by their weights.

Visualization and Analysis

To evaluate and envision STP:

• Network and Spanning Tree Visualization: Demonstration the original network topology and enlighten active links in the spanning tree, validating the eradication of loops.
• Metrics Tracking: Estimate parameters such as total path cost and make sure that there is only one path among any two nodes in the spanning tree.

Extending the Simulation

For a more cutting-edge STP simulation:

• Root Bridge Re-election: Execute root bridge re-election according to node ID or additional condition, in which the nodes can advertise themselves by way of candidates.
• Link Failures and Recovery: Replicate link failures by eliminating specific links, then rerun Dijkstra’s techniques to recalculate the spanning tree.
• Network Conditions: Adapt link weights enthusiastically to replicate network changes, validating STP’s response to diverse conditions.

In the conclusion, you can simulate Spanning Tree Protocol and evaluate their performance using MATLAB analysis tool. If needed more information regarding the Spanning Tree Protocol will offered it in another simulation setup.

Approach phdprime.com for timely simulations of Spanning Tree Protocol projects using MATLAB. We promise top-notch results with thorough explanations, and we handle multiple switches (nodes) and links for you.