García-Magariño, I.; Nasralla, MM.; Lloret, J. (2021). A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems. IEEE Network. 35(1):156-162. https://doi.org/10.1109/MNET.011.2000296
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/188190
Title:
|
A Repository of Method Fragments for Agent-Oriented Development of Learning-Based Edge Computing Systems
|
Author:
|
García-Magariño, Iván
Nasralla, Moustafa M.
Lloret, Jaime
|
UPV Unit:
|
Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
|
Issued date:
|
|
Abstract:
|
[EN] The upcoming avenue of IoT, with its massive generated data, makes it really hard to train centralized systems with machine learning in real time. This problem can be addressed with learning-based edge computing systems ...[+]
[EN] The upcoming avenue of IoT, with its massive generated data, makes it really hard to train centralized systems with machine learning in real time. This problem can be addressed with learning-based edge computing systems where the learning is performed in a distributed way on the nodes. In particular, this work focuses on developing multi-agent systems for implementing learning-based edge computing systems. The diversity of methodologies in agent-oriented software engineering reflects the complexity of developing multi-agent systems. The division of the development processes into method fragments facilitates the application of agent-oriented methodologies and their study. In this line of research, this work proposes a database for implementing a repository of method fragments considering the development of learning-based edge computing systems and the information recommended by the FIPA technical committee. This repository makes method fragments available from different methodologies, and computerizes certain metrics and queries over the existing method fragments. This work compares the performance of several combinations of dimensionality reduction methods and machine learning techniques (i.e., support vector regression, k-nearest neighbors, and multi-layer perceptron neural networks) in a simulator of a learning-based edge computing system for estimating profits and customers.
[-]
|
Subjects:
|
Support vector machines
,
Measurement
,
Neural networks
,
Real-time systems
,
Edge computing
,
Multi-agent systems
,
Software engineering
|
Copyrigths:
|
Reserva de todos los derechos
|
Source:
|
IEEE Network. (issn:
0890-8044
)
|
DOI:
|
10.1109/MNET.011.2000296
|
Publisher:
|
Institute of Electrical and Electronics Engineers
|
Publisher version:
|
https://doi.org/10.1109/MNET.011.2000296
|
Project ID:
|
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88327-R/ES/DISEÑO COLABORATIVO PARA LA PROMOCION DEL BIENESTAR EN CIUDADES INTELIGENTES INCLUSIVAS/
info:eu-repo/grantAgreement/CYTED//518RT0558/
info:eu-repo/grantAgreement/PSU//52-2020//Utilisation of IoT and sensors in smart cities for improving quality of life of impaired people/
|
Thanks:
|
The authors acknowledge PSU Smart Systems Engineering Lab, project "Utilisation of IoT and sensors in smart cities for improving quality of life of impaired people" (ref. 52-2020), CYTED (ref. 518RT0558), and the Spanish ...[+]
The authors acknowledge PSU Smart Systems Engineering Lab, project "Utilisation of IoT and sensors in smart cities for improving quality of life of impaired people" (ref. 52-2020), CYTED (ref. 518RT0558), and the Spanish Council of Science, Innovation and Universities (TIN2017-88327-R).
[-]
|
Type:
|
Artículo
|