🤖 Bringing IoT and AI Together in One Smart System

You’ve learned about IoT, dashboards, automation, and even machine learning — now it’s time to put it all into action!

In this guide, we’ll walk through how to build your first AIoT project — that’s Artificial Intelligence of Things — where your Arduino can sense, think, and respond all on its own.

“AIoT is where your devices stop waiting for commands — and start making decisions.”

---

💡 What Is an AIoT Project?

An AIoT project combines the connectivity of the Internet of Things (IoT) with the intelligence of Artificial Intelligence (AI).

That means your project can:
✅ Collect data from sensors
✅ Send it to the cloud for monitoring
✅ Use machine learning to make smart decisions
✅ Act automatically in real time

In simple terms: IoT connects, AI decides.

---

⚙️ Project Overview: Smart Environment Monitor

We’ll build a Smart Environment Monitor — a device that measures temperature and humidity, predicts comfort levels, and controls a fan automatically.

Feature	Function
Sense	Measure temperature and humidity
Think	Predict comfort level (TinyML model)
Act	Turn fan on/off automatically
Connect	Send live data to IoT Cloud dashboard

You can expand this later into smart homes, weather stations, or greenhouse systems.

---

🧰 What You’ll Need

Component	Purpose
Arduino Nano 33 BLE Sense	Main board with sensors + AI support
DHT22 Sensor (optional)	External temperature/humidity sensor
Relay Module	To control fan or motor
Fan or LED	Indicator for comfort level
USB Cable & Wi-Fi Access	For power and cloud connection
Arduino IoT Cloud Account	To visualize and automate

Optional tools: Edge Impulse (for TinyML training).

---

🧠 How It Works

1️⃣ Sensor Data → Reads temperature & humidity.
2️⃣ AI Model → Predicts if the environment is “Comfortable,” “Warm,” or “Hot.”
3️⃣ Control Logic → Turns fan ON if “Hot.”
4️⃣ Cloud Dashboard → Displays live readings and fan state.

Everything happens automatically once the board is powered up.

---

⚡ Step 1: Collect and Send Data

Use Arduino IoT Cloud or the serial monitor to gather sample readings:

#include <Arduino_HTS221.h>  // Built-in sensor on Nano 33 BLE Sense

void setup() {
  Serial.begin(9600);
  if (!HTS.begin()) {
    Serial.println("Sensor error!");
    while (1);
  }
}

void loop() {
  float temp = HTS.readTemperature();
  float hum = HTS.readHumidity();
  Serial.print("Temp: "); Serial.print(temp);
  Serial.print(" °C, Hum: "); Serial.println(hum);
  delay(1000);
}

Once you have data, you can train an AI model based on comfort zones.

---

🧩 Step 2: Train a TinyML Model

1. Go to Edge Impulse.
2. Upload temperature/humidity data and label it (e.g., cool, comfortable, hot).
3. Train a simple classification model.
4. Export the model as Arduino Library (.zip).
5. Import it into your sketch using Sketch → Include Library → Add .ZIP Library.

Your Arduino can now “understand” your data patterns!

---

⚙️ Step 3: Add AI Inference and Control

#include <your_model_inferencing.h>
#include <Arduino_HTS221.h>

void setup() {
  Serial.begin(9600);
  HTS.begin();
  pinMode(2, OUTPUT); // Fan or LED
}

void loop() {
  float t = HTS.readTemperature();
  float h = HTS.readHumidity();

  signal_t signal;
  float input[2] = {t, h};
  signal.total_length = 2;
  signal.get_data = [](size_t offset, size_t length, float *out) {
    memcpy(out, input + offset, length * sizeof(float));
    return 0;
  };

  ei_impulse_result_t result;
  run_classifier(&signal, &result, false);

  Serial.print("Prediction: ");
  Serial.println(result.classification[0].label);

  if (strcmp(result.classification[0].label, "hot") == 0) {
    digitalWrite(2, HIGH);
  } else {
    digitalWrite(2, LOW);
  }

  delay(2000);
}

The Arduino reads data, makes a prediction, and reacts automatically.

---

📊 Step 4: Visualize in Arduino IoT Cloud

Create a new Thing in Arduino IoT Cloud
and add:

• Temperature variable (float)
• Humidity variable (float)
• Fan state variable (boolean)

Connect your Nano 33 BLE Sense and publish readings every few seconds.
You can then add gauges, charts, and switches to monitor everything live.

---

💬 Step 5: Test and Expand

Try these upgrades:

• Add motion detection to trigger AI when someone’s nearby.
• Log data to the cloud for trend prediction.
• Integrate voice or app control.
• Use Portenta H7 for multi-zone automation.

“Start simple, then let your imagination scale it up.”

---

🔐 Security Tips

• Use secure connections (TLS) when sending to the cloud.
• Protect your tokens in Arduino Cloud Secrets.
• Never store passwords in plain text in your sketch.

---

🧩 Project Workflow Summary

Stage	Tool / Action
Collect	Arduino sensor readings
Train	Edge Impulse (TinyML model)
Deploy	Upload model to Arduino
Connect	Send data to IoT Cloud
Visualize	Dashboard + automation

“AIoT = Sensing + Learning + Doing.”

---

💬 Final Thoughts

You’ve just built your first AIoT system — a connected device that not only monitors but also learns and reacts intelligently.

This is the foundation of modern smart systems — responsive, predictive, and connected.

“AIoT is where creativity meets intelligence — and it starts with one Arduino.”