To simulate the distance vector routing using MATLAB platform that is a distributed routing algorithm in which each node sustains a table or distance vector of the shortest distances to every other node within the network. Every single node periodically distributes their distance vector including its neighbors, and updates their own table according to the received data. Nodes converge to the shortest paths to each other over time.
In this MATLAB replication, we will design a basic Distance Vector Routing (DVR) protocol in which each node independently updates their distance vector rely on the Bellman-Ford update rule.
Steps to Simulate Distance Vector Routing in MATLAB
- Define the Network Topology and Link Costs:
- Utilize an adjacency matrix to denote the network. Each entry within the matrix signifies the cost of the link amongst nodes; a huge amount (like inf) denotes no direct link between nodes.
% Number of nodes
numNodes = 5;
infCost = inf; % Use infinity to represent no direct link
% Define adjacency matrix for link costs
linkCosts = [0 3 infCost 7 infCost;
3 0 2 1 infCost;
infCost 2 0 2 3;
7 1 2 0 1;
infCost infCost 3 1 0];
% Display network link costs for verification
disp(‘Initial Network Link Costs:’);
disp(linkCosts);
- Initialize Distance Vectors for Each Node:
- Every single node’s distance vector will signify the minimum distance to all other nodes. Introduce each node’s vector with infinity for unknown distances, excepting for the distance to itself that is zero.
% Initialize distance vectors for each node
distanceVectors = inf(numNodes, numNodes); % Each row represents a node’s distance vector
for i = 1:numNodes
distanceVectors(i, i) = 0; % Distance to itself is zero
end
- Implement the Distance Vector Update Rule (Bellman-Ford):
- For each node, verify every neighbor to modernize their distance vector. This function will be named in each iteration to update each node’s distance vector.
function [updatedDistances, changed] = updateDistanceVector(node, distanceVectors, linkCosts)
numNodes = size(distanceVectors, 1);
updatedDistances = distanceVectors(node, :);
changed = false;
% Apply the Bellman-Ford update rule
for neighbor = 1:numNodes
if linkCosts(node, neighbor) < inf % Neighbor is directly reachable
for dest = 1:numNodes
% Calculate distance via neighbor
newDist = distanceVectors(neighbor, dest) + linkCosts(node, neighbor);
if newDist < updatedDistances(dest)
updatedDistances(dest) = newDist;
changed = true;
end
end
end
end
end
- Run the Distance Vector Routing Simulation Until Convergence:
- For each iteration, request the updateDistanceVector function for each node. If no updates happen over every node that the algorithm has converged.
maxIterations = 10; % Maximum iterations for convergence
for iter = 1:maxIterations
disp([‘Iteration ‘, num2str(iter)]);
anyChanges = false;
% Update distance vectors for each node
for node = 1:numNodes
[updatedDistances, changed] = updateDistanceVector(node, distanceVectors, linkCosts);
if changed
anyChanges = true;
distanceVectors(node, 🙂 = updatedDistances;
end
disp([‘Node ‘, num2str(node), ‘ Distance Vector: ‘, num2str(distanceVectors(node, :))]);
end
% Check for convergence
if ~anyChanges
disp(‘Distance Vectors have converged.’);
break;
end
disp(‘————————–‘);
end
- Display Final Distance Vectors:
- When the algorithm has converged then indicates the final distance vectors for each node. These denote the shortest distances from each node to every other node.
disp(‘Final Distance Vectors after Convergence:’);
for i = 1:numNodes
disp([‘Node ‘, num2str(i), ‘ Distance Vector: ‘, num2str(distanceVectors(i, :))]);
end
- Visualize the Network and Paths (Optional):
- Design the network to envision nodes, links, and the computed minimum-cost paths.
% Define node positions for visualization
nodePositions = [10 10; 20 10; 20 20; 10 20; 15 25];
figure;
hold on;
% Plot network links and link costs
for i = 1:numNodes
for j = i+1:numNodes
if linkCosts(i, j) < infCost
plot([nodePositions(i, 1), nodePositions(j, 1)], [nodePositions(i, 2), nodePositions(j, 2)], ‘k–‘);
midPoint = (nodePositions(i, 🙂 + nodePositions(j, :)) / 2;
text(midPoint(1), midPoint(2), num2str(linkCosts(i, j)), ‘HorizontalAlignment’, ‘center’, ‘BackgroundColor’, ‘white’);
end
end
plot(nodePositions(i, 1), nodePositions(i, 2), ‘bo’); % Nodes
text(nodePositions(i, 1), nodePositions(i, 2), num2str(i), ‘VerticalAlignment’, ‘bottom’);
end
title(‘Network Topology with Link Costs’);
hold off;
Explanation of Key Components
- Distance Vectors: Every single node sustains a distance vector signifying the minimum cost to attain every other node. The distance vector is updated iteratively according to the neighbors’ data.
- Bellman-Ford Update Rule: For each neighbor, each node compute the cost of attaining every destination via that neighbor and updates their distance vector if a shorter path is discovered.
- Convergence: The algorithm ends when every nodes have converged to steady distance vectors, which signifying the shortest paths.
- Visualization: Envisioning the network topology can support explain the network structure and check computed distances.
Possible Extensions
- Dynamic Link Costs: Replicate modification within link costs or link failures to monitor how nodes update its distance vectors in response.
- Track Convergence Speed: Assess how much iteration is needed for convergence in diverse network topologies that illustrates the algorithm’s scalability.
- Add Path History: Save the preceding node for each path within the distance vector, which enabling for whole path reconstruction instead of only the minimum cost.
By following above procedure you can grasp concepts and simulation idea to model and simulate the Distance Routing Projects in MATLAB environment. We will deliver any more information of this topic, if desired.
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