Code

% IoT Edge Processing for Dengue Control using MATLAB

% Step 1: Load the environmental data
% Replace this with actual sensor data collection
temperature = [30, 31, 29, 32, 33, 34]; % Example temperature data
humidity = [80, 85, 82, 81, 84, 83]; % Example humidity data

% Step 2: Preprocessing on edge (Filter noise or irrelevant data)
smoothed_temperature = movmean(temperature, 3); % Smooth the data
smoothed_humidity = movmean(humidity, 3);

% Step 3: Feature extraction on the edge
avg_temp = mean(smoothed_temperature); % Extract average temperature
avg_humidity = mean(smoothed_humidity); % Extract average humidity

% Step 4: Simple threshold-based analysis on edge device
% These thresholds could be derived from research about dengue outbreaks
if avg_temp > 30 && avg_humidity > 80
    warning('Potential Dengue Outbreak Risk Detected!');
else
    disp('No immediate Dengue risk.');
end

% Step 5: Cloud-based processing (assuming data sent to cloud)
% Here, we simulate cloud processing with machine learning models

% Assuming cloud runs a predictive model (e.g., logistic regression, SVM)
% Load a pre-trained machine learning model (You would train this separately)
% Model should predict outbreak risk based on multiple variables

% Example using a random classifier for demonstration
outbreak_risk = randi([0, 1]); % Random 0 or 1 to simulate ML model prediction

if outbreak_risk == 1
    disp('High risk of Dengue outbreak. Take action!');
    % Trigger actions like spraying insecticides, etc.
else
    disp('Low risk of Dengue outbreak.');
end

% Step 6: Visualization
figure;
subplot(2, 1, 1);
plot(temperature, '-o');
title('Temperature Data');
xlabel('Time');
ylabel('Temperature (°C)');

subplot(2, 1, 2);
plot(humidity, '-o');
title('Humidity Data');
xlabel('Time');
ylabel('Humidity (%)');