Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2649
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dc.contributor.authorAkbaş, Ayhan-
dc.contributor.authorYıldız, Hüseyin Uğur-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorTavlı, Bülent-
dc.date.accessioned2019-12-25T14:01:58Z-
dc.date.available2019-12-25T14:01:58Z-
dc.date.issued2019-08
dc.identifier.citationAkbas, A., Yildiz, H. U., Ozbayoglu, A. M., and Tavli, B. (2019). Neural network based instant parameter prediction for wireless sensor network optimization models. Wireless Networks, 25(6), 3405-3418.en_US
dc.identifier.issn1022-0038
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2649-
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs11276-018-1808-y-
dc.description.abstractOptimal operation configuration of a Wireless Sensor Network (WSN) can be determined by utilizing exact mathematical programming techniques such as Mixed Integer Programming (MIP). However, computational complexities of such techniques are high. As a remedy, learning algorithms such as Neural Networks (NNs) can be utilized to predict the WSN settings with high accuracy with much lower computational cost than the MIP solutions. We focus on predicting network lifetime, transmission power level, and internode distance which are interrelated WSN parameters and are vital for optimal WSN operation. To facilitate an efficient solution for predicting these parameters without explicit optimizations, we built NN based models employing data obtained from an MIP model. The NN based scalable prediction model yields a maximum of 3% error for lifetime, 6% for transmission power level error, and internode distances within an accuracy of 3m in prediction outcomes.en_US
dc.language.isoenen_US
dc.publisherSpringer New York LLCen_US
dc.relation.ispartofWireless networksen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWireless sensor networksen_US
dc.subjectneural networksen_US
dc.subjectmulti-layer perceptronen_US
dc.subjectbackpropagationen_US
dc.subjectmaximum lifetimeen_US
dc.subjectlifetime predictionen_US
dc.subjecttransmission power levelen_US
dc.subjectinternode distanceen_US
dc.titleNeural Network Based Instant Parameter Prediction for Wireless Sensor Network Optimization Modelsen_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.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.volume25
dc.identifier.issue6
dc.identifier.startpage3405
dc.identifier.endpage3418
dc.authorid0000-0001-7998-5735-
dc.authorid0000-0002-9615-1983-
dc.identifier.wosWOS:000471071100031en_US
dc.identifier.scopus2-s2.0-85051541898en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.institutionauthorTavlı, Bülent-
dc.identifier.doi10.1007/s11276-018-1808-y-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence 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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