How to start Implementing matlab code in wsn

I am a post-graduate student focusing on Wireless Sensor Networks. I tried to construct a wsn using the traditional energy model and k-medoid for cluster formation and cluster head selection, but it seems that I could not find a solution even using ChatGPT. For the metrics like Number of alive and dead nodes, Network lifetime, Throughput , etc. I could not understand how to get the values used for each metric so I was wondering if there is a guideline for a fresh student in this field that could help me to start implementing the code.

Thank you in advance.

Answer 1

[Umar]

Hi Ahmed,

I can concur with that statement if you did not follow the following steps throughly, Retrieve the energy consumption data for each sensor. Calculate the residual energy for each sensor by subtracting the consumed energy from the initial energy. Aggregate the residual energy values for sensors within each cluster to obtain the total energy for that cluster. Determine the cluster head and calculate its total energy based on the sum of the sensor energies within the cluster. Repeat the process for all clusters to establish the complete energy model.

Hope this helps answer your question.

Answer 2

[Umar]

Hi Ahmad,

I can guide you on how to implement matlab code for wsn. Firstly, generate random sensor data.

>> % Generate random sensor data numSensors = 100; sensorData = rand(numSensors, 2); % Assuming 2D sensor coordinates

Then, implement K-Means Clustering

>> % Specify the number of clusters (k) k = 5;

% Perform k-means clustering [idx, C] = kmeans(sensorData, k);

Afterwards, visualize Clusters

>> % Plot the sensor data with clusters figure; gscatter(sensorData(:,1), sensorData(:,2), idx); hold on; plot(C(:,1), C(:,2), 'kx', 'MarkerSize', 15, 'LineWidth', 3); legend('Cluster 1', 'Cluster 2', 'Cluster 3', 'Cluster 4', 'Cluster 5', 'Centroids'); title('K-Means Clustering for WSN Cluster Formation');

Finally, print out results.

>> % Display cluster assignments disp('Cluster Assignments:'); disp(idx);

% Display cluster centroids disp('Cluster Centroids:'); disp(C);

Hope by following these steps p, it will help you get started in the right direction.

[Ahmed Abdallah]

Hi Umar,

Thank you for answering my question and taking of your precious time.

I was wondering if that was correct since I could not calculate the residual energy for each sensor and the whole energy model for each cluster and cluster head.

[Ahmed Abdallah]

I am using the following code in case that could provide any necessary information:

numNodes = 100;

numClusters = 5;

% Generate random sensor nodes (x, y) coordinates

nodes = rand(numNodes, 2) * 100; % Assuming the deployment area is 100x100

% Randomly initialize medoids

medoids = nodes(randperm(numNodes, numClusters), :);

% Initialize variables

prevMedoids = zeros(size(medoids));

idx = zeros(numNodes, 1);

maxIter = 100; % Maximum number of iterations

tol = 1e-4; % Tolerance for convergence

% k-medoids algorithm

for iter = 1:maxIter

% Assign each node to the nearest medoid

for i = 1:numNodes

distances = sum((medoids - nodes(i, :)).^2, 2);

[~, idx(i)] = min(distances);

end

% Update medoids

prevMedoids = medoids;

for j = 1:numClusters

clusterNodes = nodes(idx == j, :);

if ~isempty(clusterNodes)

distances = pdist2(clusterNodes, clusterNodes, 'euclidean');

totalDistances = sum(distances, 2);

[~, minIdx] = min(totalDistances);

medoids(j, :) = clusterNodes(minIdx, :);

end

end

% Check for convergence

if max(max(abs(medoids - prevMedoids))) < tol

break;

end

end

% Plot the sensor nodes

figure;

scatter(nodes(:, 1), nodes(:, 2), 'b');

hold on;

% Highlight the medoids (cluster heads)

scatter(medoids(:, 1), medoids(:, 2), 100, 'r', 'filled');

% Plot lines to cluster heads

for i = 1:numNodes

plot([nodes(i, 1), medoids(idx(i), 1)], [nodes(i, 2), medoids(idx(i), 2)], 'k:');

end

% Display cluster head numbers

for i = 1:numClusters

text(medoids(i, 1), medoids(i, 2), num2str(i), 'VerticalAlignment', 'bottom', 'HorizontalAlignment', 'right', 'FontSize', 12, 'Color', 'k', 'FontWeight', 'bold');

end

title('Random Sensor Nodes Deployed with K-Medoids Clustering');

xlabel('X Coordinate');

ylabel('Y Coordinate');

legend('Sensor Nodes', 'Cluster Heads');

grid on;

hold off;