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Article

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

1
UFRN-PPgEEC, Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, Brazil
2
UNIBS-DIE, Department of Information Engineering, University of Brescia, 25123 Brescia, Italy
*
Authors to whom correspondence should be addressed.
Academic Editors: Eyhab Al-Masri, Chi-Hua Chen, Alireza Souri and Olivera Kotevska
Sensors 2021, 21(12), 4153; https://doi.org/10.3390/s21124153
Received: 8 May 2021 / Revised: 14 June 2021 / Accepted: 15 June 2021 / Published: 17 June 2021
(This article belongs to the Special Issue Internet of Things Data Analytics (IoTDA))
Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases. View Full-Text
Keywords: internet of things; online data compression; TinyML; eccentricity; evolving algorithm; LPWAN internet of things; online data compression; TinyML; eccentricity; evolving algorithm; LPWAN
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MDPI and ACS Style

Signoretti, G.; Silva, M.; Andrade, P.; Silva, I.; Sisinni, E.; Ferrari, P. An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity. Sensors 2021, 21, 4153. https://doi.org/10.3390/s21124153

AMA Style

Signoretti G, Silva M, Andrade P, Silva I, Sisinni E, Ferrari P. An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity. Sensors. 2021; 21(12):4153. https://doi.org/10.3390/s21124153

Chicago/Turabian Style

Signoretti, Gabriel, Marianne Silva, Pedro Andrade, Ivanovitch Silva, Emiliano Sisinni, and Paolo Ferrari. 2021. "An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity" Sensors 21, no. 12: 4153. https://doi.org/10.3390/s21124153

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