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Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks
The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data set must be...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073539/ https://www.ncbi.nlm.nih.gov/pubmed/33923847 http://dx.doi.org/10.3390/s21082823 |
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author | Shin, Hong-Gi Choi, Yong-Hoon Yoon, Chang-Pyo |
author_facet | Shin, Hong-Gi Choi, Yong-Hoon Yoon, Chang-Pyo |
author_sort | Shin, Hong-Gi |
collection | PubMed |
description | The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data set must be sequential. However, Wi-Fi fingerprinting only saves the received signal strength indicator for a location, so it cannot be used as RNN data. For this reason, we propose a movement path data generation technique that generates data for an RNN model for sequential positioning from Wi-Fi fingerprint data. Movement path data can be generated by creating an adjacency list for Wi-Fi fingerprint location points. However, creating an adjacency matrix for all location points requires a large amount of computation. This problem is solved by dividing indoor environment by K-means clustering and creating a cluster transition matrix based on the center of each cluster. |
format | Online Article Text |
id | pubmed-8073539 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80735392021-04-27 Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks Shin, Hong-Gi Choi, Yong-Hoon Yoon, Chang-Pyo Sensors (Basel) Communication The recurrent neural network (RNN) model, which is a deep-learning network that can memorize past information, is used in this paper to memorize continuous movements in indoor positioning to reduce positioning error. To use an RNN model in Wi-Fi-fingerprint based indoor positioning, data set must be sequential. However, Wi-Fi fingerprinting only saves the received signal strength indicator for a location, so it cannot be used as RNN data. For this reason, we propose a movement path data generation technique that generates data for an RNN model for sequential positioning from Wi-Fi fingerprint data. Movement path data can be generated by creating an adjacency list for Wi-Fi fingerprint location points. However, creating an adjacency matrix for all location points requires a large amount of computation. This problem is solved by dividing indoor environment by K-means clustering and creating a cluster transition matrix based on the center of each cluster. MDPI 2021-04-16 /pmc/articles/PMC8073539/ /pubmed/33923847 http://dx.doi.org/10.3390/s21082823 Text en © 2021 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 | Communication Shin, Hong-Gi Choi, Yong-Hoon Yoon, Chang-Pyo Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks |
title | Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks |
title_full | Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks |
title_fullStr | Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks |
title_full_unstemmed | Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks |
title_short | Movement Path Data Generation from Wi-Fi Fingerprints for Recurrent Neural Networks |
title_sort | movement path data generation from wi-fi fingerprints for recurrent neural networks |
topic | Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8073539/ https://www.ncbi.nlm.nih.gov/pubmed/33923847 http://dx.doi.org/10.3390/s21082823 |
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