Visualize Sensor Data from ESP32-CAM Video over Websockets using MongoDB, Chart.js, and node.js

ESP32-CAM Video over Websockets: Sensor Visualization with MongoDB, Chart.js, and node.js

In this article, we will explore how to create a robust and efficient system for visualizing sensor data from an ESP32-CAM video stream using Websockets, MongoDB, Chart.js, and node.js.

The ESP32-CAM is a popular microcontroller that includes a camera, making it an ideal platform for monitoring and streaming video from remote locations. However, the real power of the ESP32-CAM lies in its ability to capture and transmit sensor data in real-time, allowing for the visualization and analysis of environmental and physical data.

To create a system for sensor visualization, we will be using Websockets to transmit real-time data from the ESP32-CAM to a web application. This will allow us to visualize the data as it is collected, providing valuable insights and analysis.

First, we need to set up a server using node.js to handle the Websocket connections and store the incoming sensor data. We can use MongoDB, a popular NoSQL database, to store and manage the data in a scalable and efficient manner.

To start, let’s create a simple HTML file to host our web application:

“`html

// Chart.js code will be added here

“`

Next, let’s use Chart.js to create a simple line chart to visualize the sensor data. Add the following JavaScript code to the HTML file:

“`html

const ctx = document.getElementById(‘sensorChart’).getContext(‘2d’);
const sensorChart = new Chart(ctx, {
type: ‘line’,
data: {
labels: [],
datasets: [{
label: ‘Sensor Data’,
data: [],
borderColor: ‘rgb(255, 99, 132)’,
borderWidth: 1
}]
},
options: {
responsive: false,
scales: {
x: {
type: ‘linear’
}
}
}
});

“`

Now that we have a simple line chart set up, we can use Websockets to transmit sensor data from the ESP32-CAM to the web application. In our node.js server, we can use the `ws` library to handle Websocket connections and transmit the sensor data:

“`javascript
const WebSocket = require(‘ws’);
const wss = new WebSocket.Server({ port: 8080 });

wss.on(‘connection’, function connection(ws) {
ws.on(‘message’, function incoming(data) {
// Parse and store the sensor data in MongoDB
// Update the line chart with the new sensor data
});
});
“`

With the server set up, we can now transmit the sensor data from the ESP32-CAM to the web application using Websockets. As the data is received, we can parse and store it in MongoDB, updating the line chart in real-time to visualize the sensor data.

In conclusion, by combining the ESP32-CAM, Websockets, MongoDB, Chart.js, and node.js, we can create a powerful system for visualizing and analyzing sensor data in real-time. This system can be used for a wide range of applications, including environmental monitoring, industrial automation, and smart infrastructure. With the ability to visualize sensor data as it is collected, we can gain valuable insights and make informed decisions based on real-time information.