Avaliação de Algoritmos de Compressão de Séries Temporais Multivariadas com TinyML em Dispositivos Embarcados Article Swipe

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Morsinaldo Medeiros , Hagi Costa , Marianne Silva , Ivanovitch Silva ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.5753/courb.2025.8828

A transmissão contínua de dados em aplicações automotivas no contexto de Internet das Coisas (IoT) enfrenta desafios relacionados à largura de banda e consumo energético. Neste cenário, o TinyML — a aplicação de modelos de aprendizado de máquina em dispositivos de baixo consumo energético — emerge como uma solução. Este artigo avalia dois algoritmos de compressão de séries temporais, o Multivariate Parallel Tiny Anomaly Compressor (MPTAC) e o Multivariate Sequential Tiny Anomaly Compressor (MSTAC), com foco na sua implementação em dispositivos embarcados com recursos limitados. Deste modo, por meio de um estudo de caso realizado em um cenário real, utilizando o dispositivo OBD-II Edge Freematics One+ conectado a um veículo em movimento, os resultados indicam que o MPTAC oferece melhor fidelidade na reconstrução dos dados, enquanto o MSTAC atinge uma maior taxa de compressão, mas com maior perda de precisão. A escolha do algoritmo ideal depende do equilíbrio desejado entre compressão e qualidade dos dados reconstruídos.

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Title: Avaliação de Algoritmos de Compressão de Séries Temporais Multivariadas com TinyML em Dispositivos Embarcados

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Language: pt

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Publication year: 2025

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Publication date: 2025-05-19

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Authors: Morsinaldo Medeiros, Hagi Costa, Marianne Silva, Ivanovitch Silva

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Landing page: https://doi.org/10.5753/courb.2025.8828

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PDF URL: https://sol.sbc.org.br/index.php/courb/article/download/35255/35045

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