Autor según el artículo: Abdel-Nasser M; Mahmoud K; Omer OA; Lehtonen M; Puig D

Departamento: Enginyeria Informàtica i Matemàtiques

Autor/es de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi

Palabras clave: Time-series analysis Rnn Lstm Link quality prediction Gru Deep learning

Resumen: © 2020 Faculty of Engineering, Alexandria University Wireless community networks (WCNs) are large, heterogeneous, dynamic, and decentralized networks. Such complex characteristics raise different challenges, such as the effect of wireless communications on the performance of networks and routing protocols. The prediction approaches of link quality (LQ) can improve the performance of routing algorithms of WCNs while avoiding weak links. The prediction of LQ in WCNs can be a complex task because of the fluctuated nature of LQ measurements due to the dynamic wireless environment. In this paper, a deep learning based approach is proposed to accurately predict LQ in WCNs. Specifically, we propose the use of two variants of deep recurrent neural network (RNN): long short-term memory recurrent neural networks (LSTM-RNN) and gated recurrent unit (GRU). The positive feature of the proposed variants is that they can handle the fluctuating nature of LQ due to their ability to learn and exploit the context in LQ time-series. The experimental results on data collected from a real-world WCN show that the proposed LSTM-RNN and GRU models accurately predict LQ in WCNs compared to related methods. The proposed approach could be a helpful tool for accurately predicting LQ, thereby improving the performance of routing protocols of WCNs.

Áreas temáticas: General engineering Farmacia Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all)

Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/

ISSN: 1110-0168

Direcció de correo del autor: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat

Identificador del autor: 0000-0002-1074-2441 0000-0002-0562-4205

Fecha de alta del registro: 2023-05-20

Versión del articulo depositado: info:eu-repo/semantics/publishedVersion

Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S1110016820302519?via%3Dihub

URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/

Referencia al articulo segun fuente origial: Alexandria Engineering Journal. 59 (5): 3531-3543

Referencia de l'ítem segons les normes APA: Abdel-Nasser M; Mahmoud K; Omer OA; Lehtonen M; Puig D (2020). Link quality prediction in wireless community networks using deep recurrent neural networks. Alexandria Engineering Journal, 59(5), 3531-3543. DOI: 10.1016/j.aej.2020.05.037

DOI del artículo: 10.1016/j.aej.2020.05.037

Entidad: Universitat Rovira i Virgili

Año de publicación de la revista: 2020

Tipo de publicación: Journal Publications