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FogML-SDK

SDK implements FogML [https://github.com/tszydlo/FogML] machine learning algorithms for resource-constrained devices.

Available modules and algorithms:

• anomaly_rt

  • reservoir sampling
  • Local Outliner Factor

• classifier

  • header file for the source code generated with FogML

• dsp

  • digital signal processing algorithms for initial time series analysis

• ports

  • code needed to port the SDK to various frameworks - supports Arduino and Zephyr OS

• rl

  • header files for the reinforcement learning source code generated with FogML

Examples:

• Arduino - application that detects anomalies and classifies the fan rotation speed
  • https://github.com/tszydlo/FogML-Arduino
• Zephyr OS - application that detects gestures performed by the device
  • https://github.com/tszydlo/FogML-Zephyr
• Zephyr OS + LwM2M + NB-IoT - TinyML connected via LwM2M protocol over NB-IoT
  • https://github.com/tszydlo/FogML-Zephyr-LwM2M