AI System for IoT (Internet of Things)
Billions of IoT devices generate terabytes of data. Sending everything to cloud — expensive, slow, unsafe. AI on edge enables decisions at data source: in sensor, gateway, or local server.
IoT + AI Architecture
Three Processing Levels:
Device Level (MCU): TinyML on STM32, ESP32, Arduino. Simple classifiers, anomaly detection on raw data. Consumption <1 W.
Edge Level (Gateway): NVIDIA Jetson, Raspberry Pi 5, Intel NUC. More complex models, aggregation from multiple devices, local solutions, partial cloud upload.
Cloud Level: historical analysis, model retraining, complex event processing.
Typical AI Tasks in IoT
Predictive Maintenance: Vibration sensors on equipment → edge ML → equipment failure prediction 2–4 weeks ahead. LSTM / CNN-1D on time series.
Quality Control: Camera on conveyor → YOLOv8 on Jetson → defect detection in real-time. <30 ms latency.
Energy Management: Smart meters → edge aggregation → ML optimization of consumption.
Security: Cameras with on-device face detection → only events (not raw video) to cloud.
Protocols and Standards
MQTT for lightweight messaging. OPC-UA for industrial IoT. Matter for consumer smart home. LoRaWAN for long distances with low power consumption.
Pipeline: 8–16 weeks
Depends on device count, data types, and AI task complexity.