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Link quality prediction in wireless community networks using deep recurrent neural networks - imarina:6626715

Autor/s de la URV:Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
Autor segons l'article:Abdel-Nasser, Mohamed; Mahmoud, Karar; Omer, Osama A; Lehtonen, Matti; Puig, Domenec
Adreça de correu electrònic de l'autor:mohamed.abdelnasser@urv.cat
domenec.puig@urv.cat
Identificador de l'autor:0000-0002-1074-2441
0000-0002-0562-4205
Any de publicació de la revista:2020
Tipus de publicació:Journal Publications
ISSN:1110-0168
Referència de l'ítem segons les normes APA:Abdel-Nasser, Mohamed; Mahmoud, Karar; Omer, Osama A; Lehtonen, Matti; Puig, Domenec (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
Referència a l'article segons font original:Alexandria Engineering Journal. 59 (5): 3531-3543
Resum:© 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.
DOI de l'article:10.1016/j.aej.2020.05.037
Enllaç font original:https://www.sciencedirect.com/science/article/pii/S1110016820302519?via%3Dihub
Versió de l'article dipositat:info:eu-repo/semantics/publishedVersion
Accès a la llicència d'ús:https://creativecommons.org/licenses/by/3.0/es/
Departament:Enginyeria Informàtica i Matemàtiques
URL Document de llicència:https://repositori.urv.cat/ca/proteccio-de-dades/
Àrees temàtiques:General engineering
Farmacia
Engineering, multidisciplinary
Engineering (miscellaneous)
Engineering (all)
Paraules clau:Time-series analysis
Rnn
Lstm
Link quality prediction
Gru
Deep learning
Entitat:Universitat Rovira i Virgili
Data d'alta del registre:2024-10-12
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