How to Start Hierarchical Routing Projects Using MATLAB

To stimulate the Hierarchical routing has been includes the splitting of network into sectors or clusters to enhance the scalability and efficiency. It is widely used in large networks such as wireless sensor networks (WSNs) and mobile ad hoc networks (MANETs). Here’s a step-by-step guide to starting a Hierarchical Routing Project using MATLAB:

Steps to Start Hierarchical Routing Projects Using MATLAB

  1. Understand Hierarchical Routing
  • Nodes are grouped into clusters, by every cluster has handled through a cluster head (CH).
  • Cluster heads transmission by other CHs or a base station (BS).
  • Hierarchical routing decreases the communication overhead via finding the data aggregation and communication to CHs.
  1. Set Project Objectives

State the determination of your project:

  • Hierarchical routing is replicating the detailed network topology.
  • Apply the clustering technique for sample LEACH, HEED, PEGASIS.
  • Estimate the parameter metrics such as energy consumption, scalability, and latency.
  1. Define Network Topology

Signify the network as a 2D plane in which the nodes are placed arbitrarily or uniformly.

Example:

% Parameters

numNodes = 100; % Number of nodes

fieldSize = [100, 100]; % Field dimensions (meters)

% Generate random node positions

nodePositions = rand(numNodes, 2) .* fieldSize;

% Visualize the network

scatter(nodePositions(:,1), nodePositions(:,2), ‘filled’);

xlabel(‘X (m)’); ylabel(‘Y (m)’);

title(‘Network Topology’);

  1. Implement Clustering Algorithm

Select clustering methods for hierarchical routing:

(a) LEACH (Low-Energy Adaptive Clustering Hierarchy)

  • The Nodes are obtained the presence of cluster head to balance for an energy usage.
  • Example implementation:

% Clustering using LEACH

function [clusters, clusterHeads] = leachClustering(nodePositions, numClusters)

numNodes = size(nodePositions, 1);

clusters = cell(1, numClusters);

clusterHeads = randperm(numNodes, numClusters); % Random CH selection

for i = 1:numNodes

% Assign each node to the nearest cluster head

distances = vecnorm(nodePositions(i, 🙂 – nodePositions(clusterHeads, :), 2, 2);

[~, clusterIdx] = min(distances);

clusters{clusterIdx} = [clusters{clusterIdx}, i];

end

end

(b) HEED (Hybrid Energy-Efficient Distributed Clustering)

  • Deliberate the remaining energy and node closeness for the cluster formation.

(c) PEGASIS (Power-Efficient Gathering in Sensor Information Systems)

  • The data send the nodes form a chain.
  1. Hierarchical Routing Implementation
  • The Data flows start the nodes to their cluster head and before the base station.
  • Sample for LEACH:

% Routing from nodes to base station

baseStation = [50, 50]; % Base station position

[clusters, clusterHeads] = leachClustering(nodePositions, 5);

% Visualize clustering

figure;

scatter(nodePositions(:,1), nodePositions(:,2), ‘filled’);

hold on;

scatter(nodePositions(clusterHeads,1), nodePositions(clusterHeads,2), ‘r’, ‘filled’);

scatter(baseStation(1), baseStation(2), ‘k’, ‘filled’);

title(‘LEACH Clustering’);

legend(‘Nodes’, ‘Cluster Heads’, ‘Base Station’);

  1. Simulate Communication
  • Used the adjacency matrices or other data architecture we signify the communication connection.
  • Sample:

% Communication simulation

for clusterIdx = 1:length(clusters)

cluster = clusters{clusterIdx};

chPos = nodePositions(clusterHeads(clusterIdx), :);

% Nodes to cluster head

for node = cluster

line([nodePositions(node,1), chPos(1)], [nodePositions(node,2), chPos(2)], ‘Color’, ‘b’);

end

% Cluster head to base station

line([chPos(1), baseStation(1)], [chPos(2), baseStation(2)], ‘Color’, ‘r’, ‘LineStyle’, ‘–‘);

end

  1. Evaluate Metrics

Calculate the key performance metrics:

  • Energy Efficiency: Number of energy usage in the network.
  • Latency: Time taken for data we reach the base station for the latency.
  • Scalability: Improve the efficiency for the network size.
  1. Add Dynamic Features
  • Establish the mobility we replicate the dynamic networks.
  • Maintain the node failures through re-clustering or rerouting.
  1. Extend the Project
  • Associate the several hierarchical routing algorithms.
  • Enhance the advanced features such as multi-level hierarchies or energy-aware routing for this extend.
  1. Document and Visualize
  • The documents are considered the Visualize clusters, routing paths, and energy levels over time.
  • Example:

% Energy level visualization

energyLevels = rand(1, numNodes); % Example energy levels

scatter(nodePositions(:,1), nodePositions(:,2), 50, energyLevels, ‘filled’);

colorbar;

title(‘Node Energy Levels’);

  1. Analyze Results
  • Discuss the trade-offs among energy efficiency, scalability, and latency.
  • Focus on the advantage for the hierarchical routing in large-scale networks.

Let me know if you need a detailed implementation of clustering, routing, or energy evaluation for your hierarchical routing project!

In this manual, we entirely understood the concept of Hierarchical routing that were executed in MATLAB simulation that has generate the routing and then establish the cluster head to mimic the communication and then run the execution to analyse the outcome. In case more queries are needed we will use another document.