An Evolving Multivariate Time Series Compression Algorithm for IoT Applications

The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 22; p. 7273
Main Authors: Costa, Hagi, Silva, Marianne, Sánchez-Gendriz, Ignacio, Viegas, Carlos M. D., Silva, Ivanovitch
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 14.11.2024
MDPI
Subjects:
Algorithms
Case studies
Data analysis
Data compression
Data processing
Data transmission
Datasets
Embedded systems
Energy consumption
Energy efficiency
Environmental monitoring
Equipment and supplies
evolving algorithm
Internet of Things
IoT
Machine learning
OBD-II edge
Sensors
Teaching
Time series
TinyML
Vehicles
Wavelet transforms
United States
New Jersey
data compression
evolving algorithm
TinyML
OBD-II edge
IoT
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges as a promising solution, enabling the execution of machine-learning models on resource-constrained embedded devices. This paper aims to develop two online multivariate compression approaches specifically designed for TinyML, utilizing the Typicality and Eccentricity Data Analytics (TEDA) framework. The proposed approaches are based on data eccentricity and do not require predefined mathematical models or assumptions about data distribution, thereby optimizing compression performance. The methodology involves applying the approaches to a case study using the OBD-II Freematics ONE+ dataset, which is focused on vehicle monitoring. Results indicate that both proposed approaches, whether parallel or sequential compression, show significant improvements in execution time and compression errors. These findings highlight the approach’s potential to enhance the performance of embedded IoT systems, thereby improving the efficiency and sustainability of vehicular applications.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24227273