Articles producció científica> Enginyeria Informàtica i Matemàtiques

Link quality prediction in wireless community networks using deep recurrent neural networks

  • Dades identificatives

    Identificador: imarina:6626715
  • Autors:

    Abdel-Nasser M
    Mahmoud K
    Omer OA
    Lehtonen M
    Puig D
  • Altres:

    Autor segons l'article: Abdel-Nasser M; Mahmoud K; Omer OA; Lehtonen M; Puig D
    Departament: Enginyeria Informàtica i Matemàtiques
    Autor/s de la URV: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Paraules clau: Time-series analysis Rnn Lstm Link quality prediction Gru Deep learning
    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.
    Àrees temàtiques: General engineering Farmacia Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all)
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1110-0168
    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
    Data d'alta del registre: 2023-05-20
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://www.sciencedirect.com/science/article/pii/S1110016820302519?via%3Dihub
    Referència a l'article segons font original: Alexandria Engineering Journal. 59 (5): 3531-3543
    Referència 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
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1016/j.aej.2020.05.037
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2020
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Engineering (Miscellaneous),Engineering, Multidisciplinary
    Time-series analysis
    Rnn
    Lstm
    Link quality prediction
    Gru
    Deep learning
    General engineering
    Farmacia
    Engineering, multidisciplinary
    Engineering (miscellaneous)
    Engineering (all)
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