Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning
| dc.contributor.author | Erdem Çakan | |
| dc.contributor.author | Aral Sahin | |
| dc.contributor.author | Mert Nakıp | |
| dc.contributor.author | Volkan Rodoplu | |
| dc.contributor.author | Cakan, Erdem | |
| dc.contributor.author | Sahin, Aral | |
| dc.contributor.author | Rodoplu, Volkan | |
| dc.contributor.author | Nakip, Mert | |
| dc.date.accessioned | 2025-10-06T17:50:31Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | We develop a Multi-Layer Perceptron (MLP) Decomposition architecture for mobile Internet Things (IoT) indoor positioning. We demonstrate the performance of our architecture on an indoor system that utilizes ultra-wideband (UWB) positioning. Our architecture outperforms the following benchmark processing techniques on the same data: MLP Linear Regression Ridge Regression Support Vector Regression and the Least Squares Method for indoor positioning. The results show that our architecture can significantly advance the positioning accuracy of indoor positioning systems and enable indoor applications such as navigation proximity marketing asset tracking collision avoidance and social distancing. © 2021 Elsevier B.V. All rights reserved. | |
| dc.description.sponsorship | et al., IEEE Circuits and Systems Society (CAS) Visual Signal Processing and Communications Technical Committee, IEEE Communications Society (ComSoc), IEEE Council on Electronic Design Automation (CEDA), IEEE Reliability Society, IEEE Signal Processing Society | |
| dc.description.sponsorship | SADE Teknoloji, Inc.; TUBITAK; Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK, (1139B411901516) | |
| dc.description.sponsorship | The industry sponsor for the TUBITAK 2209-B grant that funded this work was SADELABS (SADE Teknoloji, Inc.), Izmir, Turkey. | |
| dc.description.sponsorship | This work was funded by TUBITAK (Scientific and Technological Research Council of Turkey) under the 2209-B Grant #1139B411901516. | |
| dc.identifier.doi | 10.1109/WF-IoT51360.2021.9595282 | |
| dc.identifier.isbn | 9781665444316 | |
| dc.identifier.scopus | 2-s2.0-85119835964 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119835964&doi=10.1109%2FWF-IoT51360.2021.9595282&partnerID=40&md5=b9d4c4a0d41227a8f72af2e6945c64d4 | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/8959 | |
| dc.identifier.uri | https://doi.org/10.1109/WF-IoT51360.2021.9595282 | |
| dc.language.iso | English | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.ispartof | 7th IEEE World Forum on Internet of Things WF-IoT 2021 | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.subject | Artificial Intelligence (ai), Indoor Positioning, Internet Of Things (iot), Machine Learning (ml), Multi-layer Perceptron (mlp), Ultrawideband (uwb), Architecture, Indoor Positioning Systems, Least Squares Approximations, Machine Learning, Ultra-wideband (uwb), Artificial Intelligence, Indoor Positioning, Indoor Systems, Internet Of Thing, Mobile Internet, Multi-layer Perceptron, Multilayers Perceptrons, Performance, Ultrawideband, Internet Of Things | |
| dc.subject | Architecture, Indoor positioning systems, Least squares approximations, Machine learning, Ultra-wideband (UWB), Artificial intelligence, Indoor positioning, Indoor systems, Internet of thing, Mobile Internet, Multi-layer perceptron, Multilayers perceptrons, Performance, Ultrawideband, Internet of things | |
| dc.subject | Machine Learning (ML) | |
| dc.subject | Multi-Layer Perceptron (MLP) | |
| dc.subject | Indoor Positioning | |
| dc.subject | Ultrawideband (UWB) | |
| dc.subject | Artificial Intelligence (AI) | |
| dc.subject | Internet of Things (IoT) | |
| dc.title | Multi-Layer Perceptron Decomposition Architecture for Mobile IoT Indoor Positioning | |
| dc.type | Conference Object | |
| dspace.entity.type | Publication | |
| gdc.author.scopusid | 57352404500 | |
| gdc.author.scopusid | 57212473263 | |
| gdc.author.scopusid | 6602651842 | |
| gdc.author.scopusid | 57351811100 | |
| gdc.bip.impulseclass | C4 | |
| gdc.bip.influenceclass | C5 | |
| gdc.bip.popularityclass | C4 | |
| gdc.coar.type | text::conference output | |
| gdc.collaboration.industrial | false | |
| gdc.description.department | ||
| gdc.description.departmenttemp | [Cakan E.] Yasar University, Department of Electrical and Electronics Engineering, Izmir, Turkey; [Sahin A.] Ege University, Department of Electrical and Electronics Engineering, Izmir, Turkey; [Nakip M.] Institute of Theoretical and Applied Informatics Polish Academy of Sciences (PAN), Gliwice, Poland; [Rodoplu V.] Yasar University, Department of Electrical and Electronics Engineering, Izmir, Turkey | |
| gdc.description.endpage | 257 | |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| gdc.description.startpage | 253 | |
| gdc.identifier.openalex | W3213952670 | |
| gdc.index.type | Scopus | |
| gdc.oaire.diamondjournal | false | |
| gdc.oaire.impulse | 5.0 | |
| gdc.oaire.influence | 2.5974574E-9 | |
| gdc.oaire.isgreen | false | |
| gdc.oaire.keywords | Artificial intelligence | |
| gdc.oaire.keywords | Internet of things | |
| gdc.oaire.keywords | Indoor positioning | |
| gdc.oaire.keywords | machine learning (ML) | |
| gdc.oaire.keywords | Performance | |
| gdc.oaire.keywords | indoor positioning | |
| gdc.oaire.keywords | Multi-layer perceptron | |
| gdc.oaire.keywords | Artificial Intelligence (AI) | |
| gdc.oaire.keywords | Ultrawideband (UWB) | |
| gdc.oaire.keywords | Least squares approximations | |
| gdc.oaire.keywords | Multi-Layer Perceptron (MLP) | |
| gdc.oaire.keywords | Mobile Internet | |
| gdc.oaire.keywords | Internet of Things (IoT) | |
| gdc.oaire.keywords | Ultra-wideband (UWB) | |
| gdc.oaire.keywords | Indoor systems | |
| gdc.oaire.keywords | Architecture | |
| gdc.oaire.keywords | Machine learning | |
| gdc.oaire.keywords | Indoor positioning systems | |
| gdc.oaire.keywords | Internet of thing | |
| gdc.oaire.keywords | Multilayers perceptrons | |
| gdc.oaire.keywords | Ultrawideband | |
| gdc.oaire.popularity | 5.093541E-9 | |
| gdc.oaire.publicfunded | false | |
| gdc.openalex.collaboration | International | |
| gdc.openalex.fwci | 0.9024 | |
| gdc.openalex.normalizedpercentile | 0.74 | |
| gdc.opencitations.count | 7 | |
| gdc.plumx.crossrefcites | 1 | |
| gdc.plumx.mendeley | 10 | |
| gdc.plumx.scopuscites | 12 | |
| gdc.scopus.citedcount | 12 | |
| gdc.virtual.author | Nakip, Mert | |
| gdc.virtual.author | Rodoplu, Volkan | |
| oaire.citation.endPage | 257 | |
| oaire.citation.startPage | 253 | |
| person.identifier.scopus-author-id | Çakan- Erdem (57351811100), Sahin- Aral (57352404500), Nakıp- Mert (57212473263), Rodoplu- Volkan (6602651842) | |
| project.funder.name | Funding text 1: The industry sponsor for the TUBITAK 2209-B grant that funded this work was SADELABS (SADE Teknoloji Inc.) Izmir Turkey., Funding text 2: This work was funded by TUBITAK (Scientific and Technological Research Council of Turkey) under the 2209-B Grant #1139B411901516. | |
| relation.isAuthorOfPublication | 670a1489-4737-49fd-8315-a24932013d60 | |
| relation.isAuthorOfPublication | ce356cbe-e652-4e36-b054-ee1c30c06848 | |
| relation.isAuthorOfPublication.latestForDiscovery | 670a1489-4737-49fd-8315-a24932013d60 | |
| relation.isOrgUnitOfPublication | ac5ddece-c76d-476d-ab30-e4d3029dee37 | |
| relation.isOrgUnitOfPublication.latestForDiscovery | ac5ddece-c76d-476d-ab30-e4d3029dee37 |