TEDA-RLS: A TinyML Incremental Learning Approach for Outlier Detection and Correction

• Published in: IEEE sensors journal 2024-11, Vol.24 (22), p.38165-38173
• Main Authors: Andrade, Pedro, Silva, Marianne, Medeiros, Morsinaldo, Costa, Daniel G., Silva, Ivanovitch
• Format: Article
• Language: English
• Subjects: Algorithms; Anomaly detection; Data acquisition; Data analysis; Data models; Data processing; Edge computing; Embedded systems; Internet of Things; Internet of Things (IoT); Machine learning; Microcontrollers; on-board diagnostics (OBD-II); Outliers (statistics); Real time; Real-time systems; Sensors; smart vehicles; Streams; Tiny machine learning; Unsupervised learning
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3458917

The Internet of Things (IoT) paradigm encompasses computing and networking capabilities within electronic objects, acting as a fundamental development framework with vast potential for improving lives, enhancing industrial processes, and enabling real-time decision-making. However, as the number of connected objects increases, the infrastructure for processing and handling large volumes of data is highly impacted. In response, edge computing has been exploited as a way to bring the processing burden closer to the data sources, shifting the conventional data processing flow. As a result, a series of innovative machine learning applications has been developed for resource-constrained devices, such as microcontrollers, enabling efficient data processing on the edge and inaugurating the era of tiny machine learning (TinyML). Nevertheless, although the benefits have proven promising in different scenarios, particularly when the flexibility of embedded models meets the requisites of unsupervised learning approaches, TinyML-based applications may need to perform real-time identification of data outliers since they could waste resources and impair the expected model accuracy. In this context, this article proposes an innovative TinyML outlier detection and correction algorithm based on incremental learning. This algorithm was implemented in an on-board diagnostics (OBD-II) scanner as a proof of concept, where a microcontroller acquires real-time vehicle data to identify data outliers and perform necessary corrections, benefiting practical applications in multiple scenarios.