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- Title
Machine Learning Supported Nano-Router Localization in WNSNs.
- Authors
GULEC, Omer
- 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 NanoSensor Networks (WNSNs), nano-routers undertake the task of gathering data from the nanosensors 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.
- Subjects
MACHINE learning; DATA transmission systems; VIRTUAL networks; NETWORK routers; ELECTRONIC data processing; NANOSENSORS; ENERGY consumption
- Publication
Sakarya University Journal of Science (SAUJS) / Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2023, Vol 27, Issue 3, p590
- ISSN
1301-4048
- Publication type
Article
- DOI
10.16984/saufenbilder.1246617