Evaluation of Multivariate Time-Series Compression Algorithms with TinyML in Embedded Devices

• Morsinaldo Medeiros UFRN http://orcid.org/0000-0001-7624-5301
• Hagi Costa UFRN https://orcid.org/0000-0002-0661-0227
• Marianne Silva UFRN / UFAL https://orcid.org/0000-0002-8277-7571
• Ivanovitch Silva UFRN https://orcid.org/0000-0002-0116-6489

DOI: https://doi.org/10.5753/courb.2025.8828

Abstract

Continuous data transmission in automotive applications within the context of the Internet of Things (IoT) faces challenges related to bandwidth and energy consumption. In this scenario, TinyML — the application of machine learning models on low-power devices — emerges as a solution. This paper evaluates two time-series compression algorithms, the Multivariate Parallel Tiny Anomaly Compressor (MPTAC) and the Multivariate Sequential Tiny Anomaly Compressor (MSTAC), focusing on their implementation in embedded devices with limited resources. Through a case study conducted in a real-world scenario, using the OBD-II Edge Freematics One+ device connected to a moving vehicle, the results indicate that MPTAC offers better data reconstruction fidelity, while MSTAC achieves a higher compression rate, but with greater loss of precision. The choice of the ideal algorithm depends on the desired balance between compression and the quality of the reconstructed data.

Keywords: Internet Of Things, TinyML, Time Series Compression, Embedded Devices, Energy Efficiency

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