How to start Implementing matlab code in wsn

17 次查看（过去 30 天）
Ahmed Abdallah
Ahmed Abdallah 2024-7-2
回答： Umar 2024-7-4
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.

请先登录，再回答此问题。

采纳的回答

Umar
Umar 2024-7-4
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.

更多回答（1 个）

Umar
Umar 2024-7-2

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.

  2 个评论
Ahmed Abdallah
Ahmed Abdallah 2024-7-4
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
Ahmed Abdallah 2024-7-4
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;

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Mesh Networking 的更多信息

标签

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by