Addressing Gap between Training Data and Deployed Environment by On-Device Learning

TL;DR

This paper presents on-device semi-supervised learning for tinyML devices to adapt to environmental changes, improving accuracy and reducing communication and energy costs in IoT applications.

Contribution

It introduces a novel on-device learning algorithm for low-end edge devices, enabling real-time retraining in deployed environments.

Findings

ODL improves anomaly detection accuracy in noisy environments.

Retraining reduces communication costs and energy consumption.

The approach is implemented on wireless sensor nodes with Raspberry Pi Pico.

Abstract

The accuracy of tinyML applications is often affected by various environmental factors, such as noises, location/calibration of sensors, and time-related changes. This article introduces a neural network based on-device learning (ODL) approach to address this issue by retraining in deployed environments. Our approach relies on semi-supervised sequential training of multiple neural networks tailored for low-end edge devices. This article introduces its algorithm and implementation on wireless sensor nodes consisting of a Raspberry Pi Pico and low-power wireless module. Experiments using vibration patterns of rotating machines demonstrate that retraining by ODL improves anomaly detection accuracy compared with a prediction-only deep neural network in a noisy environment. The results also show that the ODL approach can save communication cost and energy consumption for battery-powered Internet of Things devices.