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https://hdl.handle.net/11499/52026
Title: | Machine Learning Supported Nano-Router Localization in WNSNs | Authors: | Güleç, Ömer | 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. | URI: | https://hdl.handle.net/11499/52026 https://doi.org/10.16984/saufenbilder.1246617 https://search.trdizin.gov.tr/yayin/detay/1184580 |
ISSN: | 1301-4048 2147-835X |
Appears in Collections: | İktisadi ve İdari Bilimler Fakültesi Koleksiyonu TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection |
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document (43).pdf | 2.04 MB | Adobe PDF | View/Open |
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