To simulate the Distance Vector Routing (DVR) using MATLAB, we can make a network of nodes, which periodically distribute its routing data or distance vectors with adjacent nodes. Every single node updates their routing table according to the data it receives, eventually converging to the shortest paths to every node. This protocol is same to the Routing Information Protocol (RIP) in which each node sustains a vector of distances to other nodes.
Below is a stepwise procedure for replicating the Distance Vector Routing in MATLAB:
Steps to Simulate Distance Vector Routing in MATLAB
- Define Network Topology and Initialize Distance Vectors:
- Configure the amount of nodes and the adjacency matrix to denote the network topology.
- Introduce the distance vector for each node that setting distances to direct neighbors and noticing other distances as infinite (inf).
numNodes = 5; % Number of nodes in the network
infCost = inf; % Use a large value to represent infinity in the distance matrix
% Adjacency matrix with link costs (0 for no direct link, otherwise the cost)
linkCosts = [0 2 infCost 1 infCost;
2 0 3 2 infCost;
infCost 3 0 4 1;
1 2 4 0 3;
infCost infCost 1 3 0];
% Initialize distance vectors for each node
distanceVectors = cell(numNodes, 1);
for i = 1:numNodes
% Initialize each node’s distance vector based on link costs
distanceVectors{i} = linkCosts(i, :);
end
- Simulate Distance Vector Exchange Between Nodes:
- For each time step, each node distributes their distance vector with its neighbors.
- The adjacent nodes use the Bellman-Ford update rule to recalculate its minimum distances relies on the received vectors.
maxIterations = 10; % Set a limit to prevent infinite looping if the network doesn’t converge
% Distance Vector Routing function for updating distance vectors
function updated = updateDistanceVector(node, distanceVectors, linkCosts)
updated = false;
numNodes = length(distanceVectors);
for neighbor = 1:numNodes
% Check if neighbor is directly reachable
if linkCosts(node, neighbor) < infCost && neighbor ~= node
for dest = 1:numNodes
% Calculate new distance via neighbor
newDist = distanceVectors{neighbor}(dest) + linkCosts(node, neighbor);
% Update if the new path is shorter
if newDist < distanceVectors{node}(dest)
distanceVectors{node}(dest) = newDist;
updated = true; % Mark as updated
end
end
end
end
end
- Run the Distance Vector Routing Algorithm Until Convergence:
- Every single iteration, execute the updateDistanceVector function for each node.
- Verify for convergence by observing if any updates were created. If no updates are created in iteration then the algorithm has converged.
% Simulation loop for distance vector routing
for iter = 1:maxIterations
disp([‘Iteration ‘, num2str(iter)]);
anyUpdates = false;
for node = 1:numNodes
% Update the distance vector of each node
updated = updateDistanceVector(node, distanceVectors, linkCosts);
if updated
anyUpdates = true;
end
disp([‘Node ‘, num2str(node), ‘ distance vector: ‘, num2str(distanceVectors{node})]);
end
% Check for convergence
if ~anyUpdates
disp(‘Distance vectors have converged.’);
break;
end
disp(‘———————–‘);
end
- Visualize Network Topology and Distance Vectors (Optional):
- Show the network topology including nodes and links and indicate the distance vector of each node at iteration.
% Define positions for visualization
nodePositions = [10 10; 20 10; 20 20; 10 20; 15 15];
% Plot the network topology
figure;
hold on;
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–‘);
% Display link cost
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’); % Plot nodes
text(nodePositions(i, 1), nodePositions(i, 2), num2str(i), …
‘VerticalAlignment’, ‘bottom’, ‘HorizontalAlignment’, ‘center’);
end
title(‘Network Topology for Distance Vector Routing’);
hold off;
- Analyze Convergence and Path Cost (Optional):
- Compute and show the final routing tables for each node, after convergence.
- From each node to all other nodes, we can print the shortest path cost.
disp(‘Final Distance Vectors after Convergence:’);
for node = 1:numNodes
disp([‘Node ‘, num2str(node), ‘ distance vector: ‘, num2str(distanceVectors{node})]);
end
Explanation of Key Components
- Distance Vectors: Every single node sustains a vector denoting the minimum distance to all other nodes. The vector is updated iteratively according to the data from neighbors.
- Bellman-Ford Update Rule: The key update rule computes if a new path via a neighbor is smaller than the existing path. If so, the node’s distance vector is modernized.
- Convergence Check: The protocol executes until every nodes contains its minimum distances to other nodes which showing network-wide convergence.
- Visualization: Envisioning the network topology and link costs can support to know how distance vectors broadcast and converge.
Extensions to the Simulation
- Add Link Failures and Updates: Replicate a link failure by maximizing the link cost to infinity partway via the simulation and then monitor how nodes update its distance vectors to adjust.
- Implement Split Horizon and Poison Reverse: To avoid the routing loops, simulate split horizon and poison reverse that limit distance updates from moving again to the originating node.
- Track Convergence Speed: Assess how much iteration is needed to converge under diverse network sizes or topologies that can illustrate the scalability of DVR.
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