Please use this identifier to cite or link to this item:
https://hdl.handle.net/11499/52026
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Güleç, Ömer | - |
dc.date.accessioned | 2023-08-22T18:49:08Z | - |
dc.date.available | 2023-08-22T18:49:08Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 1301-4048 | - |
dc.identifier.issn | 2147-835X | - |
dc.identifier.uri | https://hdl.handle.net/11499/52026 | - |
dc.identifier.uri | https://doi.org/10.16984/saufenbilder.1246617 | - |
dc.identifier.uri | https://search.trdizin.gov.tr/yayin/detay/1184580 | - |
dc.description.abstract | Sensing data from the environment is a basic process for the nano-sensors on the network. This sensitive data need to be transmitted to the base station for data processing. In Wireless Nano-Sensor Networks (WNSNs), nano-routers undertake the task of gathering data from the nano-sensors and transmitting it to the nano-gateways. When the number of nano-routers is not enough on the network, the data need to be transmitted by multi-hop routing. Therefore, there should be more nano-routers placed on the network for efficient direct data transmission to avoid multi-hop routing problems such as high energy consumption and network traffic. In this paper, a machine learning-supported nano-router localization algorithm for WNSNs is proposed. The algorithm aims to predict the number of required nano-routers depending on the network size for the maximum node coverage in order to ensure direct data transmission by estimating the best virtual coordinates of these nano-routers. According to the results, the proposed algorithm successfully places required nano-routers to the best virtual coordinates on the network which increases the node coverage by up to 98.03% on average and provides high accuracy for efficient direct data transmission. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.title | Machine Learning Supported Nano-Router Localization in WNSNs | en_US |
dc.type | Article | en_US |
dc.identifier.volume | 27 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.startpage | 590 | en_US |
dc.identifier.endpage | 602 | en_US |
dc.department | Pamukkale University | en_US |
dc.identifier.doi | 10.16984/saufenbilder.1246617 | - |
dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.trdizinid | 1184580 | en_US |
dc.institutionauthor | … | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 08.01. Management Information Systems | - |
Appears in Collections: | İktisadi ve İdari Bilimler Fakültesi Koleksiyonu TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection |
Files in This Item:
File | Size | Format | |
---|---|---|---|
document (43).pdf | 2.04 MB | Adobe PDF | View/Open |
CORE Recommender
Page view(s)
30
checked on May 27, 2024
Download(s)
6
checked on May 27, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.