The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nous proposons GConvLoc, une méthode de localisation intérieure basée sur les empreintes WiFi utilisant des réseaux convolutifs graphiques. En utilisant la structure graphique, nous pouvons considérer les données d'empreintes digitales des points de référence et leurs étiquettes de localisation en plus des données d'empreintes digitales du point de test au moment de l'inférence. Les résultats expérimentaux montrent que GConvLoc surpasse les méthodes de base qui n'utilisent pas de graphiques.
Dongdeok KIM
Pohang University of Science and Technology (POSTECH)
Young-Joo SUH
Pohang University of Science and Technology (POSTECH)
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Dongdeok KIM, Young-Joo SUH, "GConvLoc: WiFi Fingerprinting-Based Indoor Localization Using Graph Convolutional Networks" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 4, pp. 570-574, April 2023, doi: 10.1587/transinf.2022EDL8081.
Abstract: We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8081/_p
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@ARTICLE{e106-d_4_570,
author={Dongdeok KIM, Young-Joo SUH, },
journal={IEICE TRANSACTIONS on Information},
title={GConvLoc: WiFi Fingerprinting-Based Indoor Localization Using Graph Convolutional Networks},
year={2023},
volume={E106-D},
number={4},
pages={570-574},
abstract={We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.},
keywords={},
doi={10.1587/transinf.2022EDL8081},
ISSN={1745-1361},
month={April},}
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TY - JOUR
TI - GConvLoc: WiFi Fingerprinting-Based Indoor Localization Using Graph Convolutional Networks
T2 - IEICE TRANSACTIONS on Information
SP - 570
EP - 574
AU - Dongdeok KIM
AU - Young-Joo SUH
PY - 2023
DO - 10.1587/transinf.2022EDL8081
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2023
AB - We propose GConvLoc, a WiFi fingerprinting-based indoor localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.
ER -