An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity
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 batter...
Main Authors: | Gabriel Signoretti, Marianne Silva, Pedro Andrade, Ivanovitch Silva, Emiliano Sisinni, Paolo Ferrari |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-06-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/21/12/4153 |
Similar Items
-
DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning
by: Norah N. Alajlan, et al.
Published: (2023-06-01) -
TinyML Gamma Radiation Classifier
by: Moez Altayeb, et al.
Published: (2023-02-01) -
A Comprehensive Survey on TinyML
by: Youssef Abadade, et al.
Published: (2023-01-01) -
Robustifying the Deployment of tinyML Models for Autonomous Mini-Vehicles
by: Miguel de Prado, et al.
Published: (2021-02-01) -
Evaluation of a Machine Learning Algorithm to Classify Ultrasonic Transducer Misalignment and Deployment Using TinyML
by: Des Brennan, et al.
Published: (2024-01-01)