Articles producció científica> Enginyeria Informàtica i Matemàtiques

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

  • Identification data

    Identifier: imarina:6626715
    Handle: http://hdl.handle.net/20.500.11797/imarina6626715
  • Authors:

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

    Author, as appears in the article.: Abdel-Nasser M; Mahmoud K; Omer OA; Lehtonen M; Puig D
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Abdelnasser Mohamed Mahmoud, Mohamed / Puig Valls, Domènec Savi
    Keywords: Time-series analysis Rnn Lstm Link quality prediction Gru Deep learning
    Abstract: © 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.
    Thematic Areas: General engineering Farmacia Engineering, multidisciplinary Engineering (miscellaneous) Engineering (all)
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    ISSN: 1110-0168
    Author's mail: mohamed.abdelnasser@urv.cat domenec.puig@urv.cat
    Author identifier: 0000-0002-1074-2441 0000-0002-0562-4205
    Record's date: 2023-05-20
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S1110016820302519?via%3Dihub
    Licence document URL: http://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Alexandria Engineering Journal. 59 (5): 3531-3543
    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
    Article's DOI: 10.1016/j.aej.2020.05.037
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2020
    Publication Type: Journal Publications
  • Keywords:

    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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