What Is AIoT? Artificial Intelligence of Things Explained

AIoT (Artificial Intelligence of Things) is the combination of IoT data collection with machine learning, so that connected systems can detect patterns, predict conditions or classify what they sense. The AI part is usually a small model applied to sensor data, not a large language model.

IoT vs AIoT

Realistic examples

• Anomaly detection on motor vibration and temperature.
• Occupancy estimation from several low-cost sensors.
• Activity classification from an accelerometer.
• Short-term forecasting of energy use.

Where does the model run?

• On the device (TinyML): low latency and works offline, but very small models.
• On a gateway such as a Raspberry Pi: a good balance for most student and prototype work.
• In the cloud: easiest to update and train, but needs connectivity and moves data off site.

When AI is not the answer

If a simple threshold or a moving average solves the problem reliably, use it. Many predictive-maintenance efforts deliver most of their value through good monitoring and sensible alarm limits. Machine learning needs representative data, including examples of failures, which small projects rarely have.

Prerequisites

Python, basic statistics and an IoT data pipeline. Build the pipeline first with Raspberry Pi and Python and then add models in AIoT training. For background on the IoT side, see how IoT works.

References

• scikit-learn documentation, Outlier and novelty detection: scikit-learn.org
• TensorFlow Lite for Microcontrollers: tensorflow.org