Adaptive Edge AI Framework for Real-Time Anomaly Detection in Resource-Constrained IoT Devices

An adaptive edge AI framework for real-time anomaly detection on resource-constrained IoT devices.

An adaptive edge AI framework for real-time anomaly detection on resource-constrained IoT devices. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want to connect hardware to software: reading sensors, sending data to a back end, visualising it and acting on it with alerts or control.

The main parts are real-time processing of incoming data, sensor or device data collection, a messaging layer between devices and the server, a live dashboard, and alerts or control actions. The reference build uses Apache Kafka, ESP32, MQTT, Python, and Node-RED, with a clear project structure, setup instructions and sample data so that it can be run and extended.