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Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning
In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490743/ https://www.ncbi.nlm.nih.gov/pubmed/37687848 http://dx.doi.org/10.3390/s23177393 |
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author | Fu, Wei Peng, Qin Hu, Canwei |
author_facet | Fu, Wei Peng, Qin Hu, Canwei |
author_sort | Fu, Wei |
collection | PubMed |
description | In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm generates unnecessary redesigns and leads to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm extends the network lifecycle by 33% compared to the comparison algorithm and achieves good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations. |
format | Online Article Text |
id | pubmed-10490743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104907432023-09-09 Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning Fu, Wei Peng, Qin Hu, Canwei Sensors (Basel) Article In high-speed railway operational monitoring network systems targeting railway infrastructure as its monitoring objective, there is a wide variety of sensor types with diverse operational requirements. These systems have varying demands on data transmission latency and network lifespan. Most of the previous research focuses only on prolonging network lifetime or reducing data transmission delays when designing or optimizing routing protocols, without co-designing the two. In addition, due to the harsh operating environment of high-speed railways, when the network changes dynamically, the traditional routing algorithm generates unnecessary redesigns and leads to high overhead. Based on the actual needs of high-speed railway operation environment monitoring, this paper proposes a novel Double Q-values adaptive model combined with the existing reinforcement learning method, which considers the energy balance of the network and real-time data transmission, and constructs energy saving and delay. The two-dimensional reward avoids the extra overhead of maintaining a global routing table while capturing network dynamics. In addition, the adaptive weight coefficient is used to ensure the adaptability of the model to each business of the high-speed railway operation environment monitoring system. Finally, simulations and performance evaluations are carried out and compared with previous studies. The results show that the proposed routing algorithm extends the network lifecycle by 33% compared to the comparison algorithm and achieves good real-time data performance. It also saves energy and has fewer delays than the other three routing protocols in different situations. MDPI 2023-08-24 /pmc/articles/PMC10490743/ /pubmed/37687848 http://dx.doi.org/10.3390/s23177393 Text en © 2023 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 Fu, Wei Peng, Qin Hu, Canwei Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning |
title | Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning |
title_full | Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning |
title_fullStr | Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning |
title_full_unstemmed | Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning |
title_short | Energy-Saving Adaptive Routing for High-Speed Railway Monitoring Network Based on Improved Q Learning |
title_sort | energy-saving adaptive routing for high-speed railway monitoring network based on improved q learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490743/ https://www.ncbi.nlm.nih.gov/pubmed/37687848 http://dx.doi.org/10.3390/s23177393 |
work_keys_str_mv | AT fuwei energysavingadaptiveroutingforhighspeedrailwaymonitoringnetworkbasedonimprovedqlearning AT pengqin energysavingadaptiveroutingforhighspeedrailwaymonitoringnetworkbasedonimprovedqlearning AT hucanwei energysavingadaptiveroutingforhighspeedrailwaymonitoringnetworkbasedonimprovedqlearning |