Management of IoT TinyML Devices
summary
A concrete pipeline integrates Edge Impulse model generation with LwM2M integration over Zephyr RTOS to run TinyML on a constrained device: a fan plus an accelerometer that classifies vibrations and flags anomalies in real time. The demo shows OTA model updates, telemetry and device management multiplexed on one connection, uses five management objects (model, pattern detector, classifier, anomaly analyzer, etc.), and demonstrates remote retraining and successful live anomaly detection.
AI-written summary · about
chapters (6)
- 0:00Introduction to TinyML
- 0:42Understanding TinyML
- 5:35Anomaly detection demo
- 7:20Lightweight M2M protocol
- 10:37Integrating TinyML with LwM2M
- 16:29Q&A and conclusion
related talks
54mSmarter Everything with ML & the IoT
25mTinyML as-a-Service
36mTinyML at the Edge: Deploying and Optimizing AI Workloads on Zephyr RTOS
01h 01mML and the IoT: Living on the Edge
20mHow ML is Powering the Future of IoT
45mAIoT Ops - Using Kubernetes and ML Ops to Build Edge ML Applications
45mUsing Edge Machine Learning to save power and development time
49mEnable IoT with Edge Computing and Machine Learning