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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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 | |
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700 | 1 | |a Choi, Yong-Hoon |e verfasserin |4 aut | |
700 | 1 | |a Yoon, Chang-Pyo |e verfasserin |4 aut | |
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