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An Enhanced Food Digestion Algorithm for Mobile Sensor Localization
Mobile sensors can extend the range of monitoring and overcome static sensors’ limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorit...
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/PMC10490790/ https://www.ncbi.nlm.nih.gov/pubmed/37687962 http://dx.doi.org/10.3390/s23177508 |
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author | Chu, Shu-Chuan Shao, Zhi-Yuan Zhong, Ning Liu, Geng-Geng Pan, Jeng-Shyang |
author_facet | Chu, Shu-Chuan Shao, Zhi-Yuan Zhong, Ning Liu, Geng-Geng Pan, Jeng-Shyang |
author_sort | Chu, Shu-Chuan |
collection | PubMed |
description | Mobile sensors can extend the range of monitoring and overcome static sensors’ limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results. |
format | Online Article Text |
id | pubmed-10490790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104907902023-09-09 An Enhanced Food Digestion Algorithm for Mobile Sensor Localization Chu, Shu-Chuan Shao, Zhi-Yuan Zhong, Ning Liu, Geng-Geng Pan, Jeng-Shyang Sensors (Basel) Article Mobile sensors can extend the range of monitoring and overcome static sensors’ limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results. MDPI 2023-08-29 /pmc/articles/PMC10490790/ /pubmed/37687962 http://dx.doi.org/10.3390/s23177508 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 Chu, Shu-Chuan Shao, Zhi-Yuan Zhong, Ning Liu, Geng-Geng Pan, Jeng-Shyang An Enhanced Food Digestion Algorithm for Mobile Sensor Localization |
title | An Enhanced Food Digestion Algorithm for Mobile Sensor Localization |
title_full | An Enhanced Food Digestion Algorithm for Mobile Sensor Localization |
title_fullStr | An Enhanced Food Digestion Algorithm for Mobile Sensor Localization |
title_full_unstemmed | An Enhanced Food Digestion Algorithm for Mobile Sensor Localization |
title_short | An Enhanced Food Digestion Algorithm for Mobile Sensor Localization |
title_sort | enhanced food digestion algorithm for mobile sensor localization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10490790/ https://www.ncbi.nlm.nih.gov/pubmed/37687962 http://dx.doi.org/10.3390/s23177508 |
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