Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6613
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dc.contributor.authorYılmaz, Muhammed-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorTavlı, Bülent-
dc.date.accessioned2021-09-11T15:42:58Z-
dc.date.available2021-09-11T15:42:58Z-
dc.date.issued2021en_US
dc.identifier.issn1022-0038-
dc.identifier.issn1572-8196-
dc.identifier.urihttps://doi.org/10.1007/s11276-021-02556-8-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6613-
dc.description.abstractThe most important quality-of-service metric for wireless sensor networks (WSNs), arguably, is the lifetime. Estimating the network lifetime under optimal operation conditions can be done by casting the problem as a mixed integer programming (MIP) model and solving the problem instances to optimality. Yet, solution times of such models are excessively high. Therefore, it is not possible to work with large problem instances within an acceptable solution time. Adopting learning based algorithms has the ability to produce near-optimal results much more rapidly in comparison to MIP models. In this study, we propose a deep neural network (DNN) based model to determine the WSN lifetime near-optimally virtually instantly. The proposed model is able to predict the lifetime of a randomly deployed WSN over a predetermined area with an average accuracy more than 98.5%. An interesting outcome of the study is that the DNN based model is able to estimate the lifetime of WSNs with higher number of nodes successfully even if it is trained with a dataset obtained with lower number of nodes.en_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofWireless Networksen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWireless sensor networksen_US
dc.subjectNetwork lifetimeen_US
dc.subjectLifetime predictionen_US
dc.subjectMachine learningen_US
dc.subjectDeep neural networksen_US
dc.subjectMulti-layer perceptronen_US
dc.titleEfficient computation of wireless sensor network lifetime through deep neural networksen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Artificial Intelligence Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Yapay Zeka Mühendisliği Bölümütr_TR
dc.identifier.volume27en_US
dc.identifier.issue3en_US
dc.identifier.startpage2055en_US
dc.identifier.endpage2065en_US
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000616893800001en_US
dc.identifier.scopus2-s2.0-85100812662en_US
dc.institutionauthorYılmaz, Muhammed-
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.institutionauthorTavlı, Bülent-
dc.identifier.doi10.1007/s11276-021-02556-8-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ1-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextnone-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
crisitem.author.dept02.5. Department of Electrical and Electronics Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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