Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression Technique

• Tipo: Artigo em Revista Científica Internacional
• Data: 2024

Autores

Andrade, P (Autor) Outra; Silva, I (Autor) Outra; Diniz, M (Autor) Outra; Flores, T (Autor) Outra; Costa, DG (Autor) FEUP; Soares, E (Autor) Outra

Revista

Título: Transactions on Embedded Computing Systems

Vol. 23

ISSN: 1539-9087

Editora: ACM

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-00Z-K2C

DOI: 10.1145/3591356

Abstract (EN): The Internet of Things (IoT) has made it possible to include everyday objects in a connected network, allowing them to intelligently process data and respond to their environment. Thus, it is expected that those objectswill gain an intelligent understanding of their environment and be able to process data more efficiently than before. Particularly, such edge computing paradigm has allowed the execution of inference methods on resource-constrained devices such as microcontrollers, significantly changing the way IoT applications have evolved in recent years. However, although this scenario has supported the development of Tiny Machine Learning (TinyML) approaches on such devices, there are still some challenges that require further investigation when optimizing data streaming on the edge. Therefore, this article proposes a new unsupervised TinyML regression technique based on the typicality and eccentricity of the samples to be processed. Moreover, the proposed technique also exploits a Recursive Least Squares (RLS) filter approach. Combining all these features, the proposed method uses similarities between samples to identify patterns when processing data streams, predicting outcomes based on these patterns. The results obtained through the extensive experimentation utilizing vehicular data streams were highly encouraging. The proposed algorithm was meticulously compared with the RLS algorithm and Convolutional Neural Networks (CNN). It exhibited significantly superior performance, with mean squared errors that were 4.68 and 12.02 times lower, respectively, compared to the aforementioned techniques.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 28