How to Start Distributed Routing Projects Using MATLAB

To start Distributed Routing using MATLAB which is method in which nodes create routing decisions in a network, that frequently to utilise the local data or via interaction with its neighbors. MATLAB environment can be utilised for replicating distributed routing protocols for wireless sensor networks (WSNs), ad-hoc networks, and more networks. Below is a stepwise approach how to start a Distributed Routing Project in MATLAB:

Steps to Start Distributed Routing Project in MATLAB

  1. Understand Distributed Routing

Distributed routing protocols based on:

  • Local Decision-Making: Every single node chooses their routing path to utilise data from neighbors.
  • Communication Overhead: Nodes periodically or on-demand swap routing data.
  • Instances like Distance Vector Routing (RIP), Dynamic Source Routing (DSR), and Ad-hoc On-Demand Distance Vector (AODV).
  1. Set Project Objectives

Specify the project’s objectives like:

  • Replicate the distributed routing protocols.
  • Measure the performance parameters such as convergence time, packet delivery ratio, and routing overhead.
  • Execute the custom distributed routing algorithm.
  1. Define the Network Topology

With the help of an adjacency matrix or an edge list to signify the network like graph.

Example: Adjacency Matrix

% Define adjacency matrix

adjMatrix = [

0 1 0 0 1;

1 0 1 0 0;

0 1 0 1 1;

0 0 1 0 1;

1 0 1 1 0

];

% Visualize the network

G = graph(adjMatrix, ‘upper’);

plot(G);

title(‘Network Topology’)

  1. Choose or Design a Distributed Routing Protocol

According to the project aims, we choose a protocol:

(a) Distance Vector Routing (RIP)

Every single node sustains a table of distances to other nodes and swaps updates periodically including neighbors.

Algorithm:

  • Nodes distribute its distance vectors including neighbors.
  • Every single node modernizes their routing table to utilise the Bellman-Ford equation: D(i,j)=min⁡k{D(i,k)+D(k,j)}D(i, j) = \min_k \{D(i, k) + D(k, j)\}D(i,j)=kmin​{D(i,k)+D(k,j)}
  • Do again awaiting no changes arise (convergence).

Implementation:

function [routingTable] = distanceVectorRouting(adjMatrix, numNodes)

% Initialize distance vectors

routingTable = inf(numNodes, numNodes);

for i = 1:numNodes

routingTable(i, i) = 0; % Distance to self is 0

end

% Exchange information iteratively

for iter = 1:100

for i = 1:numNodes

for j = 1:numNodes

if adjMatrix(i, j) > 0 % Neighbor

for k = 1:numNodes

routingTable(i, k) = min(routingTable(i, k), routingTable(j, k) + adjMatrix(i, j));

end

end

end

end

end

end

(b) Dynamic Source Routing (DSR)

A reactive protocol in which routes are determined as required. Nodes maintain caches of learned routes.

Implementation:

  • Mimic route discovery using propagating demands.
  • Store the discovered route in future usage.
  1. Simulate the Routing Process

We need to execute the routing logic to utilise selected protocol.

Example: Distance Vector Routing

% Simulate Distance Vector Routing

numNodes = size(adjMatrix, 1);

routingTable = distanceVectorRouting(adjMatrix, numNodes);

% Display the final routing table

disp(‘Final Routing Table:’);

disp(routingTable);

  1. Evaluate the Protocol

Examine the performance parameters like:

  • Convergence Time: We measure the volume of iterations to become stable for the routing table.
  • Routing Overhead: Compute the amount of messages that are swapped effectively.
  • Path Optimality: Confirm that paths are minimum, which is called optimal route.
  1. Visualize Results

Envision the determined paths at network graph.

Example:

% Visualize the shortest path from Node 1 to Node 5

src = 1; dest = 5;

[path, dist] = shortestpath(graph(adjMatrix), src, dest);

% Plot the graph with highlighted path

plot(G, ‘EdgeLabel’, G.Edges.Weight);

highlight(plot(G), path, ‘EdgeColor’, ‘r’, ‘LineWidth’, 2);

title(‘Shortest Path’);

  1. Extend the Project

We will want to prolong the simulation by:

  • Launching dynamic conditions such as node/link failures, mobility.
  • To equate the distributed routing including centralized routing.
  • Executing advanced protocols such as AODV, DSR, or ZRP.
  1. Document the Project
  • It offers more insights like MATLAB code, explanations, and visualizations.
  • Deliberate outcomes and possible enhancements.

Complete Example Code

Below is a comprehensive code of replicating Distance Vector Routing:

% Define adjacency matrix

adjMatrix = [

0 1 0 0 1;

1 0 1 0 0;

0 1 0 1 1;

0 0 1 0 1;

1 0 1 1 0

];

% Number of nodes

numNodes = size(adjMatrix, 1);

% Simulate Distance Vector Routing

function [routingTable] = distanceVectorRouting(adjMatrix, numNodes)

routingTable = inf(numNodes, numNodes);

for i = 1:numNodes

routingTable(i, i) = 0;

end

for iter = 1:100

for i = 1:numNodes

for j = 1:numNodes

if adjMatrix(i, j) > 0

for k = 1:numNodes

routingTable(i, k) = min(routingTable(i, k), routingTable(j, k) + adjMatrix(i, j));

end

end

end

end

end

end

% Run the protocol

routingTable = distanceVectorRouting(adjMatrix, numNodes);

% Display the routing table

disp(‘Final Routing Table:’);

disp(routingTable);

% Visualize the shortest path

G = graph(adjMatrix);

plot(G);

title(‘Network Topology’);

src = 1; dest = 5;

[path, dist] = shortestpath(G, src, dest);

highlight(plot(G), path, ‘EdgeColor’, ‘r’, ‘LineWidth’, 2);

title(‘Shortest Path’);

In this project, we offers basic method to simulate and estimate the Distributed Routing Projects in MATLAB tool that has sample snippets, visualization, outcomes and improvements of this project. If you want any more assistance, we will provide it too.