Machine-Learning-Based Classification of Electronic Devices Using an IoT Smart Meter
This study investigates the implementation of artificial intelligence (AI) algorithms on resource-constrained edge devices, such as ESP32 and Raspberry Pi, within the context of smart grid (SG) applications. Specifically, it proposes a smart-meter-based system capable of classifying and detecting th...
Saved in:
Main Authors: | Paulo Eugênio da Costa Filho, Leonardo Augusto de Aquino Marques, Israel da S. Felix de Lima, Ewerton Leandro de Sousa, Márcio Eduardo Kreutz, Augusto V. Neto, Eduardo Nogueira Cunha, Dario Vieira |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-05-01
|
Series: | Informatics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-9709/12/2/48 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhancing DevOps Practices in the IoT–Edge–Cloud Continuum: Architecture, Integration, and Software Orchestration Demonstrated in the COGNIFOG Framework
by: Kostas Petrakis, et al.
Published: (2025-04-01) -
Intelligent Data Reduction for IoT: A Context-Driven Framework
by: Laercio Pioli, et al.
Published: (2025-01-01) -
A Design of Workflow-Based Automated Failure Recovery Framework in IoT Edge Environment
by: Phuong Bac Ta, et al.
Published: (2025-01-01) -
Improving Far-Edge Device Management in IoT Applications Using Kubernetes
by: Carlos Resende, et al.
Published: (2025-01-01) -
The IoT/IoE Integrated Security & Safety System of Pompeii Archeological Park
by: Alberto Bruni, et al.
Published: (2025-06-01)