Advancements in Machine Learning in Sensor Systems: Insights from Sensys-ML and TinyML Communities

TY - GEN

T1 - Advancements in Machine Learning in Sensor Systems

T2 - 3rd IEEE Workshop on Machine Learning on Edge in Sensor Systems, SenSys-ML 2024

AU - Yadav, Poonam

N1 - Publisher Copyright: © 2024 IEEE.

PY - 2024/6/24

Y1 - 2024/6/24

N2 - The integration of machine learning (ML) algorithms with edge sensor systems has fundamentally transformed numerous industries. This convergence empowers real-time data processing, analysis, and decision-making at the network's periphery. This paper investigates the latest advancements in this domain by examining two key communities: Sensys-ML and TinyML. While Sensys-ML concentrates on optimizing ML for sensor systems, TinyML prioritizes deploying ML models on resource-constrained devices. Through a critical analysis of these communities' contributions and interactions, this work aims to provide a comprehensive overview of cutting-edge methodologies, persistent challenges, and promising future directions for ML at the edge within sensor systems. By tracing the trajectory of advancements in this field, we offer a critical reflection on the broader research landscape and its scope. Additionally, we identify emerging research areas as reflected in prominent forums and underscore persisting knowledge gaps that call for further investigation.

AB - The integration of machine learning (ML) algorithms with edge sensor systems has fundamentally transformed numerous industries. This convergence empowers real-time data processing, analysis, and decision-making at the network's periphery. This paper investigates the latest advancements in this domain by examining two key communities: Sensys-ML and TinyML. While Sensys-ML concentrates on optimizing ML for sensor systems, TinyML prioritizes deploying ML models on resource-constrained devices. Through a critical analysis of these communities' contributions and interactions, this work aims to provide a comprehensive overview of cutting-edge methodologies, persistent challenges, and promising future directions for ML at the edge within sensor systems. By tracing the trajectory of advancements in this field, we offer a critical reflection on the broader research landscape and its scope. Additionally, we identify emerging research areas as reflected in prominent forums and underscore persisting knowledge gaps that call for further investigation.

KW - Machine Learning

KW - Sensor systems

KW - TinyML

KW - UltraML

UR - http://www.scopus.com/inward/record.url?scp=85197740973&partnerID=8YFLogxK

U2 - 10.1109/SenSys-ML62579.2024.00009

DO - 10.1109/SenSys-ML62579.2024.00009

M3 - Conference contribution

AN - SCOPUS:85197740973

T3 - Proceedings - 2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems, SenSys-ML 2024

SP - 21

EP - 26

BT - Proceedings - 2024 IEEE 3rd Workshop on Machine Learning on Edge in Sensor Systems, SenSys-ML 2024

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 13 May 2024 through 16 May 2024

ER -