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

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorAbdel-Nasser, Mohameden_US
dc.contributor.authorMahmoud, Kararen_US
dc.contributor.authorA. Omer, Osamaen_US
dc.contributor.authorLehtonen, Mattien_US
dc.contributor.authorPuig, Domenecen_US
dc.contributor.departmentDepartment of Electrical Engineering and Automationen
dc.contributor.groupauthorPower Systems and High Voltage Engineeringen
dc.contributor.organizationUniversidad Rovira i Virgilien_US
dc.contributor.organizationAswan Universityen_US
dc.date.accessioned2021-01-27T09:10:22Z
dc.date.available2021-01-27T09:10:22Z
dc.date.issued2020-10en_US
dc.description.abstractWireless 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.en
dc.description.versionPeer revieweden
dc.format.extent13
dc.format.extent3531-3543
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationAbdel-Nasser, M, Mahmoud, K, A. Omer, O, Lehtonen, M & Puig, D 2020, ' Link quality prediction in wireless community networks using deep recurrent neural networks ', Alexandria Engineering Journal, vol. 59, no. 5, pp. 3531-3543 . https://doi.org/10.1016/j.aej.2020.05.037en
dc.identifier.doi10.1016/j.aej.2020.05.037en_US
dc.identifier.issn1110-0168
dc.identifier.otherPURE UUID: 0188ebad-a4af-471f-81d5-05639d90c34aen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/0188ebad-a4af-471f-81d5-05639d90c34aen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85087014819&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/55462844/ELEC_Abdel_Nasser_etal_Link_Quality_Prediction_AlexEngJou_2020_finalpublishedversion.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/102329
dc.identifier.urnURN:NBN:fi:aalto-202101271640
dc.language.isoenen
dc.publisherAlexandria University
dc.relation.ispartofseriesAlexandria Engineering Journalen
dc.relation.ispartofseriesVolume 59, issue 5en
dc.rightsopenAccessen
dc.subject.keywordLink quality predictionen_US
dc.subject.keywordTime-series analysisen_US
dc.subject.keywordDeep learningen_US
dc.subject.keywordRNNen_US
dc.subject.keywordLSTMen_US
dc.subject.keywordGRUen_US
dc.titleLink quality prediction in wireless community networks using deep recurrent neural networksen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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[article-cris] Sähkötekniikan korkeakoulu / ELEC