How to Simulate Path Vector Routing Using MATLAB

To simulate Path Vector Routing in MATLAB has needs to follow numerous steps and it is a protocol usually utilized in Border Gateway Protocol (BGP) for inter-domain routing in the Internet. Different distance vector routing, path vector routing not only delivers the shortest distance however it also sustains the path (sequence of nodes) to each destination. Each node distributes paths with its neighbours, and these paths include the information about the sequence of nodes traversed, mitigating loops and sustaining policy-based routing.

In this MATLAB simulation, we will design a simplified Path Vector Routing Protocol, in which each node sustains and updates a path vector to reach each other node in the network.

Steps to Simulate Path Vector Routing in MATLAB

1. Define the Network Topology and Link Costs:
   • Utilize an adjacency matrix to signify the network, in which an each entry represents the cost among nodes. Set a large number (e.g., inf) to denotes no direct link.

% Number of nodes

numNodes = 5;

infCost = inf;  % Use a large number to represent no direct connection

% Define adjacency matrix for link costs

linkCosts = [0 2 infCost 6 infCost;

2 0 3 8 5;

infCost 3 0 infCost 7;

6 8 infCost 0 9;

infCost 5 7 9 0];

% Display network link costs

disp(‘Initial Network Link Costs:’);

disp(linkCosts);

1. Initialize Path Vectors for Each Node:
   • Each node sustains a path vector which involves the cost and path to every other node. Originally, each node only distinguishes the cost to itself (zero) and to its direct neighbors.

% Initialize path vectors for each node

pathVectors = cell(numNodes, numNodes); % Each cell contains {cost, path} for each destination

for i = 1:numNodes

for j = 1:numNodes

if i == j

pathVectors{i, j} = {0, [i]}; % Cost is 0 to itself, path contains only the node

elseif linkCosts(i, j) < infCost

pathVectors{i, j} = {linkCosts(i, j), [i, j]}; % Directly reachable neighbors

else

pathVectors{i, j} = {infCost, []}; % No known path

end

end

end

1. Implement the Path Vector Update Rule:
   • In iteration, each node gets path information from its neighbors. If a shorter path to any destination is established through a neighbor, the node updates its path vector.

function [updatedPathVector, changed] = updatePathVector(node, pathVectors, linkCosts)

numNodes = size(pathVectors, 1);

updatedPathVector = pathVectors(node, :); % Start with current path vector

changed = false;

% Loop over each neighbor

for neighbor = 1:numNodes

if linkCosts(node, neighbor) < infCost % Neighbor is directly reachable

% Receive the path vector from the neighbor

for dest = 1:numNodes

if neighbor ~= dest && ~ismember(node, pathVectors{neighbor, dest}{2})

% New potential path cost

newCost = linkCosts(node, neighbor) + pathVectors{neighbor, dest}{1};

% Current known cost

currentCost = updatedPathVector{dest}{1};

% Update path if newCost is lower

if newCost < currentCost

updatedPathVector{dest} = {newCost, [node, pathVectors{neighbor, dest}{2}]};

changed = true;

end

end

end

end

end

end

1. Run the Path Vector Routing Simulation Until Convergence:
   • For each an iteration, implement the updatePathVector function for each node. The replication avoid when no updates happen via all nodes, signifying convergence.

maxIterations = 10; % Maximum number of iterations for convergence

for iter = 1:maxIterations

disp([‘Iteration ‘, num2str(iter)]);

anyChanges = false;

% Update path vectors for each node

for node = 1:numNodes

[updatedPathVector, changed] = updatePathVector(node, pathVectors, linkCosts);

if changed

anyChanges = true;

pathVectors(node, 🙂 = updatedPathVector;

end

% Display current path vector for the node

disp([‘Node ‘, num2str(node), ‘ Path Vector:’]);

for dest = 1:numNodes

if ~isempty(pathVectors{node, dest}{2})

disp([‘  To Node ‘, num2str(dest), ‘: Cost = ‘, num2str(pathVectors{node, dest}{1}), …

‘, Path = ‘, num2str(pathVectors{node, dest}{2})]);

end

end

end

% Check for convergence

if ~anyChanges

disp(‘Path Vectors have converged.’);

break;

end

disp(‘————————–‘);

end

1. Display Final Path Vectors for Each Node:
   • Once the technique has converged, demonstrate the final path vectors for each node. These vectors signify the shortest paths (with both cost and route) from each node to every other node.

disp(‘Final Path Vectors after Convergence:’);

for i = 1:numNodes

disp([‘Node ‘, num2str(i), ‘ Path Vector:’]);

for j = 1:numNodes

if ~isempty(pathVectors{i, j}{2})

disp([‘  To Node ‘, num2str(j), ‘: Cost = ‘, num2str(pathVectors{i, j}{1}), …

‘, Path = ‘, num2str(pathVectors{i, j}{2})]);

end

end

disp(‘————————–‘);

end

1. Visualize the Network and Path Vectors (Optional):
   • Plot the network, demonstrating nodes, links, and estimated paths. This envisions can deliver the insights into the network’s structure and the paths indomitable by the path vector protocol.

% 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’); % Plot 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

• Path Vector Initialization: Each node’s path vector is initialized according to its direct neighbors, setting the cost to itself by the way of zero and recording direct neighbors’ paths.
• Path Vector Update Rule: Each node validates all neighbors’ paths to see if a shorter path to any destination is available. The node updates its path vector if a shorter or better path is established.
• Convergence: The replication prevents when no updates are made via the network, that signifying all nodes have established their best paths.
• Loop Prevention: By validating if the current node is already in a neighbor’s path, we mitigate loops in the path vector.

Possible Extensions

• Policy-Based Routing: Execute constraints for prioritizing paths according to policies, like selecting certain nodes or prevent specific paths.
• Dynamic Path Costs: Mimic the variation in link costs to see how the protocol adjusts to alter and recalculates paths.
• Extended Network Size: Validate the protocol on larger networks to measure performance and convergence time.

We had gathered the information, you can explore Path Vector Routing project which will be simulated and evaluated in the MATLAB environment. If needed, we will deliver the detailed structured for entire execution process in another manual.phdprime.com is here to help you with your Path Vector Routing Projects using MATLAB. If you reach out to us, we’ll provide you with clear explanations and ensure you get great results on time. We focus on the order of nodes you travel through, avoiding loops, and keeping your project on track with policy-based routing.