Review on unmanned aerial vehicle assisted sensor node localization in wireless networks: soft computing approaches

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dc.contributor.authorAnnepu, Visalakshi
dc.contributor.authorSona, Deepika Rani
dc.contributor.authorRavikumar, Chinthaginjala V.
dc.contributor.authorBagadi, Kalapraveen
dc.contributor.authorAlibakhshikenari, Mohammad
dc.contributor.authorAlthuwayb, Ayman Abdulhadi
dc.contributor.authorAlali, Bader
dc.contributor.authorVirdee, Bal S.
dc.contributor.authorPau, Giovanni
dc.contributor.authorDayoub, Iyad
dc.contributor.authorSee, Chan H.
dc.contributor.authorFalcone Lanas, Francisco
dc.contributor.departmentIngeniería Eléctrica, Electrónica y de Comunicaciónes_ES
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.contributor.departmentIngeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzareneu
dc.date.accessioned2023-04-27T08:51:17Z
dc.date.available2023-04-27T08:51:17Z
dc.date.issued2022
dc.date.updated2023-04-27T08:42:13Z
dc.description.abstractNode positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) is preferred over localization using fixed terrestrial anchor node (FTAN) because of low implementation complexity and high accuracy. The conventional multilateration technique estimates the position of the unknown node (UN) based on the distance from the anchor node (AN) to UN that is obtained from the received signal strength (RSS) measurement. However, distortions in the propagation medium may yield incorrect distance measurement and as a result, the accuracy of RSS-multilateration is limited. Though the optimization based localization schemes are considered to be a better alternative, the performance of these schemes is not satisfactory if the distortions are non-linear. In such situations, the neural network (NN) architecture such as extreme learning machine (ELM) can be a better choice as it is a highly non-linear classifier. The ELM is even superior over its counterpart NN classifiers like multilayer perceptron (MLP) and radial basis function (RBF) due to its fast and strong learning ability. Thus, this paper provides a comparative review of various soft computing based localization techniques using both FTAN and aerial ANs for better acceptability.en
dc.description.sponsorshipDr. Mohammad Alibakhshikenari acknowledges support from the CONEX-Plus programme funded by Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 801538. This work was supported by Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (Agencia Estatal de Investigación, Fondo Europeo de Desarrollo Regional -FEDER-, European Union) under the research grant PID2021-127409OB-C31 CONDOR.en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAnnepu, V., Sona, D. R., Ravikumar, C. V., Bagadi, K., Alibakhshikenari, M., Althuwayb, A. A., Alali, B., Virdee, B. S., Pau, G., Dayoub, I., See, C. H., & Falcone, F. (2022). Review on unmanned aerial vehicle assisted sensor node localization in wireless networks: Soft computing approaches. IEEE Access, 10, 132875-132894. https://doi.org/10.1109/ACCESS.2022.3230661en
dc.identifier.doi10.1109/ACCESS.2022.3230661
dc.identifier.issn2169-3536
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/45201
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartofIEEE Access, 10, 132875-132894en
dc.relation.projectIDinfo:eu-repo/grantAgreement/European Commission/Horizon 2020 Framework Programme/ 801538/
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-127409OB-C31/
dc.relation.publisherversionhttps://doi.org/10.1109/ACCESS.2022.3230661
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License.en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectExtreme learning machineen
dc.subjectLocalizationen
dc.subjectUnmanned aerial vehiclesen
dc.subjectWireless sensor networksen
dc.titleReview on unmanned aerial vehicle assisted sensor node localization in wireless networks: soft computing approachesen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication69667b5c-e390-42d4-bc71-9f256c1b7b85
relation.isAuthorOfPublication.latestForDiscovery69667b5c-e390-42d4-bc71-9f256c1b7b85

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