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Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning

Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accurac...

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Detalles Bibliográficos
Autores principales: Guo, Yiping, Hu, Guyu, Shao, Dongsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571042/
https://www.ncbi.nlm.nih.gov/pubmed/36236790
http://dx.doi.org/10.3390/s22197691
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author Guo, Yiping
Hu, Guyu
Shao, Dongsheng
author_facet Guo, Yiping
Hu, Guyu
Shao, Dongsheng
author_sort Guo, Yiping
collection PubMed
description Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accuracy of the multi-path routing scheme, this paper innovatively proposes a multi-path routing algorithm for a wireless sensor network (WSN) based on the evaluation. First, we design and implement the real-time evaluation algorithm based on semi-supervised learning (RESL). We prove that RESL is better in evaluation time and evaluation accuracy through comparative experiments. Then, we combine RESL to design and implement the multi-path routing algorithm for wireless sensor networks based on semi-supervised learning (MRSSL). Then, we prove that MRSSL has advantages in improving the accuracy of the multi-path routing scheme through comparative experiments.
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spelling pubmed-95710422022-10-17 Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning Guo, Yiping Hu, Guyu Shao, Dongsheng Sensors (Basel) Article Multi-path transmission can well solve the data transmission reliability problems and life cycle problems caused by single-path transmission. However, the accuracy of the routing scheme generated by the existing multi-path routing algorithms is difficult to guarantee. In order to improve the accuracy of the multi-path routing scheme, this paper innovatively proposes a multi-path routing algorithm for a wireless sensor network (WSN) based on the evaluation. First, we design and implement the real-time evaluation algorithm based on semi-supervised learning (RESL). We prove that RESL is better in evaluation time and evaluation accuracy through comparative experiments. Then, we combine RESL to design and implement the multi-path routing algorithm for wireless sensor networks based on semi-supervised learning (MRSSL). Then, we prove that MRSSL has advantages in improving the accuracy of the multi-path routing scheme through comparative experiments. MDPI 2022-10-10 /pmc/articles/PMC9571042/ /pubmed/36236790 http://dx.doi.org/10.3390/s22197691 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Guo, Yiping
Hu, Guyu
Shao, Dongsheng
Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning
title Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning
title_full Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning
title_fullStr Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning
title_full_unstemmed Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning
title_short Multi-Path Routing Algorithm for Wireless Sensor Network Based on Semi-Supervised Learning
title_sort multi-path routing algorithm for wireless sensor network based on semi-supervised learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571042/
https://www.ncbi.nlm.nih.gov/pubmed/36236790
http://dx.doi.org/10.3390/s22197691
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