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Por favor, use este identificador para citar o enlazar este ítem: http://uvadoc.uva.es/handle/10324/31423
Título: Online machine learning algorithms to predict link quality in community wireless mesh networks
Autor: Bote Lorenzo, Miguel L.
Gómez Sánchez, Eduardo
Mediavilla Pastor, Carlos
Asensio Pérez, Juan Ignacio
Año del Documento: 2018
Editorial: Elsevier
Descripción: Producción Científica
Documento Fuente: Computer Networks Volume 132, 2018, Pages 68-80
Resumen: Accurate link quality predictions are key in community wireless mesh networks (CWMNs) to improve the performance of routing protocols. Unlike other techniques, online machine learning algorithms can be used to build link quality predictors that are adaptive without requiring a predeployment effort. However, the use of these algorithms to make link quality predictions in a CWMN has not been previously explored. This paper analyses the performance of 4 well-known online machine learning algorithms for link quality prediction in a CWMN in terms of accuracy and computational load. Based on this study, a new hybrid online algorithm for link quality prediction is proposed. The evaluation of the proposed algorithm using data from a real large scale CWMN shows that it can achieve a high accuracy while generating a low computational load.
Palabras Clave: Aprendizaje automático
ISSN: 1389-1286
Revisión por Pares: SI
DOI: https://doi.org/10.1016/j.comnet.2018.01.005
Patrocinador: Ministerio de Economía, Industria y Competitividad (Project TIN2014-53199-C3-2-R)
Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA082U16)
Version del Editor: https://www.sciencedirect.com/science/article/pii/S1389128618300069
Idioma: eng
URI: http://uvadoc.uva.es/handle/10324/31423
Derechos: info:eu-repo/semantics/openAccess
Aparece en las colecciones:DEP71 - Artículos de revista

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