Banca de DEFESA: HAGI JAKOBSON DANTAS DA COSTA
Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.STUDENT : HAGI JAKOBSON DANTAS DA COSTA
DATE: 19/12/2024
TIME: 14:00
LOCAL: Remoto
TITLE:
An Evolving Multivariate Time Series Compression Algorithm for IoT Applications
KEY WORDS:
Multivariate Time Series Compression, IoT, Online Algorithms, Evolving Algorithms, TinyML, OBD-II Edge.
PAGES: 60
BIG AREA: Engenharias
AREA: Engenharia Elétrica
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 time series 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. Both approaches were applied to two case studies: one using the OBD-II Freematics ONE+ dataset for vehicle monitoring in a non-embedded context, and another in an embedded scenario. 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.
COMMITTEE MEMBERS:
Presidente - 2885532 - IVANOVITCH MEDEIROS DANTAS DA SILVA
Externo ao Programa - 2249146 - CARLOS MANUEL DIAS VIEGAS - UFRNExterno à Instituição - IGNACIO SANCHEZ GENDRIZ
Externa à Instituição - MARIANNE BATISTA DINIZ DA SILVA - UFAL