RT info:eu-repo/semantics/article
T1 Online machine learning algorithms to predict link quality in community wireless mesh networks
A1 Bote Lorenzo, Miguel Luis
A1 Gómez Sánchez, Eduardo
A1 Mediavilla Pastor, Carlos
A1 Asensio Pérez, Juan Ignacio
K1 Aprendizaje automático
AB 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.
PB Elsevier
SN 1389-1286
YR 2018
FD 2018
LK http://uvadoc.uva.es/handle/10324/31423
UL http://uvadoc.uva.es/handle/10324/31423
LA eng
NO Computer Networks Volume 132, 2018, Pages 68-80
NO Producción Científica
DS UVaDOC
RD 28-abr-2024