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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...

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Detalles Bibliográficos
Autores principales: Chu, Shu-Chuan, Shao, Zhi-Yuan, Zhong, Ning, Liu, Geng-Geng, Pan, Jeng-Shyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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