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 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:21 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 21(2021), 8 vom: 16. Apr. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shin, Hong-Gi [VerfasserIn] |
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| Links: |
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| Themen: |
Data pre-processing |
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| Anmerkungen: |
Date Completed 30.04.2021 Date Revised 01.04.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/s21082823 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM324770952 |
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| 500 | |a Date Revised 01.04.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a K-means clustering | |
| 650 | 4 | |a Wi-Fi fingerprint | |
| 650 | 4 | |a data pre-processing | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a recurrent neural network | |
| 700 | 1 | |a Choi, Yong-Hoon |e verfasserin |4 aut | |
| 700 | 1 | |a Yoon, Chang-Pyo |e verfasserin |4 aut | |
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