View source for IPDL - 15: IoT-Based Small Project

== IPDL - 9A: Distance Measurement Using Arduino Uno and Ultrasonic Sensor ==

=== Objective ===
To interface an '''ultrasonic sensor''' with an '''Arduino Uno''' and measure the distance of an object from the sensor.

=== Required Components ===

* Arduino Uno
* HC-SR04 ultrasonic sensor
* USB cable
* Jumper wires

=== Circuit Diagram ===

<!-- IMAGE PLACEHOLDER:
Suggested image: Circuit diagram showing Arduino Uno connected to an HC-SR04 ultrasonic sensor.

Show:
* HC-SR04 VCC → Arduino 5V
* HC-SR04 GND → Arduino GND
* HC-SR04 TRIG → Arduino Digital Pin 9
* HC-SR04 ECHO → Arduino Digital Pin 10

Suggested filename:
Arduino-Ultrasonic-Sensor-Circuit.png
-->

=== Board Connections ===

<!-- IMAGE PLACEHOLDER:
Suggested image: Actual board connection photo showing Arduino Uno and HC-SR04 ultrasonic sensor.

Clearly show the connections to Arduino Digital Pins 9 and 10.

Suggested filename:
Arduino-Ultrasonic-Sensor-Board-Connection.png
-->

=== Pin Connections ===

<table class="wikitable" style="text-align:center; width:100%;">
<tr>
<th>Component</th>
<th>Component Pin</th>
<th>Arduino Uno Connection</th>
</tr>

<tr>
<td rowspan="4">HC-SR04 Ultrasonic Sensor</td>
<td>VCC</td>
<td>5V</td>
</tr>

<tr>
<td>GND</td>
<td>GND</td>
</tr>

<tr>
<td>TRIG</td>
<td>Digital Pin 4</td>
</tr>

<tr>
<td>ECHO</td>
<td>Digital Pin 6</td>
</tr>
</table>

=== Working Principle ===

The HC-SR04 ultrasonic sensor measures distance using ultrasonic sound waves.

The Arduino sends a short trigger pulse to the '''TRIG''' pin. The ultrasonic sensor then transmits an ultrasonic sound wave.

When the sound wave strikes an object, it is reflected back towards the sensor. The sensor measures the time required for the reflected wave to return.

The distance is calculated using:

<pre>
Distance = (Time × Speed of Sound) / 2
</pre>

The division by 2 is required because the sound wave travels from the sensor to the object and then back to the sensor.

=== Experiment Code ===

<pre>
// © 2026, Cognifront. Member of The IoT Kits Family.

// Experiment: Distance Measurement Using Arduino Uno
// HC-SR04 Ultrasonic Sensor

int trigPin = 4;
int echoPin = 6;

void setup()
{
  pinMode(trigPin, OUTPUT);
  pinMode(echoPin, INPUT);
  Serial.begin(9600);
  delay(5000);
}

void loop()
{
  digitalWrite(trigPin, LOW);
  delayMicroseconds(2);
  digitalWrite(trigPin, HIGH);            //set the trigger pin high to provide a bus;
  delayMicroseconds(10);
  digitalWrite(trigPin, LOW);
  int duration = pulseIn(echoPin, HIGH);  //used to check duration of wave (tx & rx);
  float distance = duration * 0.034 / 2;
  if(distance >= 0)
  {
    Serial.print("Distance in cm is: ");
    Serial.println(distance);
  }
  
  delay(500);
}
</pre>

=== Testing the System ===

# Connect the HC-SR04 ultrasonic sensor according to the connection table.
# Upload the program to the Arduino Uno.
# Open the '''Serial Monitor'''.
# Set the baud rate to '''9600'''.
# Place an object in front of the ultrasonic sensor.
# Observe the distance displayed in centimetres.
# Move the object closer or farther away.
# Observe the change in the displayed distance.

=== Observation ===

* The Arduino transmits a trigger signal to the ultrasonic sensor.
* The sensor measures the time taken by the ultrasonic wave to return.
* The distance is displayed in the Serial Monitor.
* Moving the object changes the measured distance.

=== Result ===

The distance of an object from the HC-SR04 ultrasonic sensor was successfully measured using Arduino Uno.

=== Conclusion ===

This experiment demonstrates distance measurement using an '''HC-SR04 ultrasonic sensor and Arduino Uno'''. The sensor uses the reflection of ultrasonic sound waves to determine the distance of an object.


== IPDL - 9B: Soil Moisture Monitoring Using Arduino Uno ==

=== Objective ===
To interface a '''soil moisture sensor''' with an '''Arduino Uno''' and monitor the moisture level of soil.

=== Required Components ===

* Arduino Uno
* Soil moisture sensor module
* USB cable
* Jumper wires

=== Circuit Diagram ===

<!-- IMAGE PLACEHOLDER:
Suggested image: Circuit diagram showing Arduino Uno connected to a soil moisture sensor module.

Show:
* Sensor VCC → Arduino 5V
* Sensor GND → Arduino GND
* Sensor AO → Arduino Analog Pin A0

Suggested filename:
Arduino-Soil-Moisture-Sensor-Circuit.png
-->

=== Board Connections ===

<!-- IMAGE PLACEHOLDER:
Suggested image: Actual board connection photo showing Arduino Uno and soil moisture sensor module.

Clearly show the analog output connected to Arduino Analog Pin A0.

Suggested filename:
Arduino-Soil-Moisture-Sensor-Board-Connection.png
-->

=== Pin Connections ===

<table class="wikitable" style="text-align:center; width:100%;">
<tr>
<th>Component</th>
<th>Component Pin</th>
<th>Arduino Uno Connection</th>
</tr>

<tr>
<td rowspan="3">Soil Moisture Sensor</td>
<td>VCC</td>
<td>5V</td>
</tr>

<tr>
<td>GND</td>
<td>GND</td>
</tr>

<tr>
<td>AO</td>
<td>Analog Pin A0</td>
</tr>
</table>

=== Working Principle ===

The soil moisture sensor measures the moisture level in soil by detecting changes in the electrical properties around the sensing probe.

The sensor provides an analog output that is read by the Arduino using Analog Pin A0.

The Arduino converts the analog signal into a value between:

<pre>
0 to 1023
</pre>

The measured value can be used to indicate whether the soil is relatively dry or wet.

'''Note:''' The actual analog values depend on the sensor type, soil type and moisture conditions. The threshold used in the program may need to be adjusted after observing the sensor values.

=== Experiment Code ===

<pre>
// © 2026, Cognifront. Member of The IoT Kits Family.

// Experiment: Soil Moisture Monitoring Using Arduino Uno
/* Change these values based on your observations */
#define wetSoil 277   // Define max value we consider soil 'wet'
#define drySoil 380   // Define min value we consider soil 'dry'

// Define analog input
#define sensorPin A0

void setup() {  
  Serial.begin(9600);
  pinMode(sensorPin, INPUT);
}

void loop() {
  // Read the Analog Input and print it
  int moisture = analogRead(sensorPin);
  Serial.print("Analog output: ");
  Serial.println(moisture);
  
  // Determine status of our soil
  if (moisture < wetSoil) {
    Serial.println("Status: Soil is too wet");
  } else if (moisture >= wetSoil && moisture < drySoil) {
    Serial.println("Status: Soil moisture is perfect");
  } else {
    Serial.println("Status: Soil is too dry - time to water!");
  }
  Serial.println();
  
  // Take a reading every second
  delay(1000);
}
</pre>

=== Testing the System ===

# Connect the soil moisture sensor according to the connection table.
# Upload the program to the Arduino Uno.
# Open the '''Serial Monitor'''.
# Set the baud rate to '''9600'''.
# Observe the moisture value when the sensor probe is dry.
# Insert the sensor probe into moist soil.
# Observe the change in the moisture value.
# Compare the readings for dry and moist conditions.

=== Observation ===

* The sensor provides an analog moisture value to the Arduino.
* The value changes according to the moisture condition around the sensor probe.
* The program displays the soil moisture value in the Serial Monitor.
* The soil is classified as '''DRY''' or '''MOIST''' according to the programmed threshold.

=== Result ===

The soil moisture level was successfully monitored using a soil moisture sensor and Arduino Uno.

=== Conclusion ===

This experiment demonstrates the basic principle of '''soil moisture monitoring using Arduino Uno'''. The analog output from the sensor is read and processed by the Arduino to indicate the condition of the soil.


== IPDL - 9C: Submersible Pump Control Using Arduino Uno ==

=== Objective ===
To control a '''submersible water pump''' using an '''Arduino Uno''' through a suitable relay module.

=== Required Components ===

* Arduino Uno
* Submersible water pump
* Single-channel relay module
* Suitable external power supply for the pump
* USB cable
* Jumper wires

=== Circuit Diagram ===

<!-- IMAGE PLACEHOLDER:
Suggested image: Circuit diagram showing Arduino Uno connected to a
single-channel relay module for controlling a low-voltage submersible water pump.

Show:
* Relay IN → Arduino Digital Pin 7
* Relay VCC → Arduino 5V
* Relay GND → Arduino GND

Also show the pump power supply switched through the relay COM and NO terminals.

Suggested filename:
Arduino-Submersible-Pump-Control-Circuit.png
-->

=== Board Connections ===

<!-- IMAGE PLACEHOLDER:
Suggested image: Actual board connection photo showing Arduino Uno,
single-channel relay module and submersible water pump power connection.

Clearly separate the Arduino control-side connections from the pump power-side connections.

Suggested filename:
Arduino-Submersible-Pump-Board-Connection.png
-->

=== Pin Connections ===

<table class="wikitable" style="text-align:center; width:100%;">
<tr>
<th>Component</th>
<th>Component Pin</th>
<th>Arduino Uno Connection</th>
</tr>

<tr>
<td rowspan="3">Single-Channel Relay</td>
<td>IN</td>
<td>Digital Pin 7</td>
</tr>

<tr>
<td>VCC</td>
<td>5V</td>
</tr>

<tr>
<td>GND</td>
<td>GND</td>
</tr>
</table>

=== Pump Power Connection ===

The submersible pump must be powered using a '''suitable power supply matching the pump voltage and current requirements'''.

The relay is used as an electrical switch:

<pre>
Pump Power Supply → Relay COM / NO → Pump
</pre>

The exact power-side wiring depends on the type and voltage rating of the pump and relay.

'''Important:''' Do not power a submersible pump directly from an Arduino output pin. The Arduino pin cannot supply the current required by the pump.

'''Safety Note:''' Always verify the voltage and current ratings of the pump, relay and power supply before making the connections.

=== Working Principle ===

The Arduino controls the relay through Digital Pin 7.

When the relay is activated, the relay completes the pump power circuit and the pump starts operating.

When the relay is deactivated, the pump power circuit is disconnected and the pump stops.

'''Note:''' This program assumes an '''active-LOW relay module'''.

* `LOW` on Digital Pin 7 → Pump ON
* `HIGH` on Digital Pin 7 → Pump OFF

=== Experiment Code ===

<pre>
// © 2026, Cognifront. Member of The IoT Kits Family.

// Experiment: Submersible Pump Control Using Arduino Uno
// Active-LOW Relay Module

#define RELAY_PIN 7

void setup() {

  Serial.begin(9600);

  pinMode(RELAY_PIN, OUTPUT);

  // Start with pump OFF
  digitalWrite(RELAY_PIN, HIGH);

  Serial.println("Submersible Pump Control Ready");
}

void loop() {

  // Turn pump ON
  Serial.println("Pump ON");

  digitalWrite(RELAY_PIN, LOW);

  delay(5000);

  // Turn pump OFF
  Serial.println("Pump OFF");

  digitalWrite(RELAY_PIN, HIGH);

  delay(5000);
}
</pre>

=== Testing the System ===

# Connect the relay module to the Arduino according to the connection table.
# Connect the pump to its suitable external power supply through the relay.
# Ensure that all voltage and current ratings are correct.
# Upload the program to the Arduino Uno.
# Open the '''Serial Monitor''' at '''9600''' baud.
# Power the pump using its appropriate power supply.
# Observe that the pump turns ON for approximately 5 seconds.
# Observe that the pump turns OFF for approximately 5 seconds.
# Verify that the ON/OFF cycle repeats continuously.

=== Observation ===

* The relay activates when the Arduino sends the ON control signal.
* The submersible pump starts operating when the relay connects the pump power circuit.
* The pump stops when the relay disconnects the power circuit.
* The Serial Monitor displays the current pump status.

=== Result ===

The submersible water pump was successfully controlled using Arduino Uno and a relay module.

=== Conclusion ===

This experiment demonstrates the basic control of a '''submersible water pump using Arduino Uno'''. The Arduino provides the control signal, while the relay safely switches the pump power circuit.

== IPDL - 9D: Real-Time Object Detection Using Raspberry Pi, USB Camera and OpenCV ==

=== Objective ===
To perform '''real-time object detection''' using a '''Raspberry Pi''', a '''USB camera''' and '''OpenCV'''.

=== Required Components ===

* Raspberry Pi 4
* USB camera
* Raspberry Pi power supply
* MicroSD card with Raspberry Pi OS
* Monitor, keyboard and mouse for setup
* Python 3 environment
* OpenCV Python library
* Pre-trained object detection model files

=== System Diagram ===

<!-- IMAGE PLACEHOLDER:
Suggested image: Block diagram showing a USB camera connected directly to
a Raspberry Pi 4.

Show:
USB Camera → Raspberry Pi 4 → OpenCV Object Detection → Display

Do not show Arduino, ultrasonic sensor, soil moisture sensor or pump.

Suggested filename:
Raspberry-Pi-USB-Camera-OpenCV-Object-Detection-System.png
-->

=== Board Connections ===

<!-- IMAGE PLACEHOLDER:
Suggested image: Actual setup photo showing only a Raspberry Pi 4 connected
to a USB camera.

Show the camera connected to a Raspberry Pi USB port and the object
detection output displayed on a monitor.

Do not show Arduino or any Arduino sensors.

Suggested filename:
Raspberry-Pi-USB-Camera-OpenCV-Board-Connection.png
-->

=== Connections ===

<table class="wikitable" style="text-align:center; width:100%;">
<tr>
<th>Component</th>
<th>Connection</th>
</tr>

<tr>
<td>USB Camera</td>
<td>Raspberry Pi USB Port</td>
</tr>

<tr>
<td>Raspberry Pi Power Supply</td>
<td>Raspberry Pi Power Input</td>
</tr>

<tr>
<td>Monitor</td>
<td>Raspberry Pi HDMI Output</td>
</tr>
</table>

=== Working Principle ===

#Install Python 3.11
#Verify Python 3.11
#Create cv_project folder
#Enter ~/cv_project
#Create cv_env using Python 3.11
#Activate cv_env
#Upgrade pip
#Install PyTorch CPU version
#Install Ultralytics
# Install remaining dependencies
# Verify YOLO installation
# Download/load yolov8n.pt
# Run image object detection test
# Check /dev/video0 camera
# Run live YOLO camera detection
# Later, reactivate the environment and run your programs

=== Set Up and running ===

Basic Setup for Object Detection. Run the commands below one by one:
Install Python 3.11
<pre>
sudo apt update
sudo apt install -y python3.11 python3.11-venv
</pre>

Check Python 3.11
<pre>
python3.11 --version
</pre>

Go to home directory
<pre>
cd ~
</pre>

Create a project folder
<pre>
mkdir -p cv_project
</pre>

Enter the project folder
<pre>
cd ~/cv_project
</pre>

Create virtual environment
<pre>
python3.11 -m venv cv_env
</pre>

Activate virtual environment
<pre>
source cv_env/bin/activate
</pre>

Upgrade pip
<pre>
python -m pip install --upgrade pip
</pre>

After this, you should see:
(cv_env) pi@raspberrypi:~/cv_project $

Install PyTorch Library from the URL
<pre>
pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
</pre>

Install Ultralytics
<pre>
pip install ultralytics --no-deps
</pre>

Install other dependencies
<pre>
pip install matplotlib pyyaml requests scipy tqdm psutil pandas ultralytics-thop
</pre>

<pre>
pip install polars ultralytics-platform
</pre>

<pre>
pip install "opencv-python>=4.7.0"
</pre>

Check if YOLO is installed successfully
<pre>
python -c "from ultralytics import YOLO; print('YOLO OK')"
</pre>

Load YOLOv8n
<pre>
python -c "from ultralytics import YOLO; model=YOLO('yolov8n.pt'); print('YOLOv8 Nano model loaded successfully')"
</pre>

Next: run actual object detection
Create a file using the console itself
<pre>
nano yolo_test.py
</pre>

Paste the complete code, copy and paste from below
<pre>
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
results = model("https://ultralytics.com/images/bus.jpg", save=True)
print("Detection completed!")
</pre>

After pasting the code, to save, Press:
Ctrl + O → Enter → Ctrl + X

Run Bash:
<pre>
python yolo_test.py
</pre>

After  successful test
1. Check the camera device
Run:
<pre>
ls /dev/video*
</pre>

If you see:
/dev/video0

2. Test live YOLOv8n detection
Create the program: Create a file using the console itself
<pre>
nano yolo_camera.py
</pre>

Paste the complete code, copy and paste from below
<pre>
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model.predict(
    source=0,
    show=True,
    conf=0.5
)
</pre>

To Save, Press:
Ctrl + O → Enter → Ctrl + X

Run Bash:
<pre>
python yolo_camera.py
</pre>

Press q to stop the live detection.

Next time you want to activate it:
<pre>
cd ~/cv_project
</pre>
<pre>
source ~/cv_project/cv_env/bin/activate
</pre>

And the run the bash files as usual

To exit the environment:
<pre>
deactivate
</pre>

=== Observation ===

The USB camera captures live video.
The Raspberry Pi processes the captured frames using OpenCV.
Supported objects are detected by the pre-trained model.
A bounding box appears around detected objects.
The object name is displayed.
The confidence percentage is displayed.
Pressing '''Q''' stops the program.

=== Result ===

Real-time object detection was successfully performed using a Raspberry Pi, USB camera and OpenCV with a pre-trained YOLO model.

=== Conclusion ===

This experiment demonstrates '''real-time computer vision and object detection using Raspberry Pi'''. The USB camera captures live video, while OpenCV and the pre-trained YOLO model process the video frames and identify supported objects.