DumbLoc: dumb indoor localization framework using WiFi fingerprinting
Extant indoor positioning techniques based on received signal strength (RSS) fingerprinting have achieved accurate positioning results. They leverage existing open-source datasets for training and comparing their model performance. However, the models trained on one building cannot be used for anoth...
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sg-ntu-dr.10356-1765742024-05-17T15:41:23Z DumbLoc: dumb indoor localization framework using WiFi fingerprinting Narasimman, Srivathsan Chakaravarthi Alphones, Arokiaswami School of Electrical and Electronic Engineering Centre for Information Sciences and Systems Engineering Dimensionality reduction Estimation Wi-Fi Fingerprinting Floor prediction Indoor localization Machine learning Indoor positioning Representation learning Training time Transfer learning Extant indoor positioning techniques based on received signal strength (RSS) fingerprinting have achieved accurate positioning results. They leverage existing open-source datasets for training and comparing their model performance. However, the models trained on one building cannot be used for another since these models learn the relationship of a specific set of RSSs to the building and floor locations necessitating the expensive, time-consuming process of fingerprinting. Even when we consider the individual datasets producing these excellent results, a lot of painstaking optimization is required which precludes a lot of people trying to implement indoor positioning quickly. Most of the input RSS vector is empty with redundant information and the static class labels used for buildings and floors make the models unusable on other buildings. We propose a machine learning-based framework that uses RSS values from the strongest access point (AP) signals and normalized output labels to combat this issue. The framework was used on the open-source UJI dataset using less than 5% of the 520 APs to achieve 94.15% and 8.45 m floor prediction accuracy and mean positioning error respectively without any optimization. This technique was reused on 10 other public datasets and achieved an average floor estimation accuracy of 91.93% when trained with new data and 88.68% without any new data compared to 87.1% of the closest competitor. Agency for Science, Technology and Research (A*STAR) Submitted/Accepted version This work was supported in part by the RIE2020 Industry Alignment Fund-Industry Collaboration Projects Funding Initiative through the Agency for Science, Technology and Research (A*STAR) under Award I1801E0020; and in part by the cash and in-kind contribution from Surbana Jurong Pte Ltd. 2024-05-16T08:03:47Z 2024-05-16T08:03:47Z 2024 Journal Article Narasimman, S. C. & Alphones, A. (2024). DumbLoc: dumb indoor localization framework using WiFi fingerprinting. IEEE Sensors Journal, 24(9), 14623-14630. https://dx.doi.org/10.1109/JSEN.2024.3374415 1530-437X https://hdl.handle.net/10356/176574 10.1109/JSEN.2024.3374415 2-s2.0-85187989201 9 24 14623 14630 en I1801E0020 IEEE Sensors Journal © 2024 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1109/JSEN.2024.3374415. application/pdf |
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Engineering Dimensionality reduction Estimation Wi-Fi Fingerprinting Floor prediction Indoor localization Machine learning Indoor positioning Representation learning Training time Transfer learning |
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Engineering Dimensionality reduction Estimation Wi-Fi Fingerprinting Floor prediction Indoor localization Machine learning Indoor positioning Representation learning Training time Transfer learning Narasimman, Srivathsan Chakaravarthi Alphones, Arokiaswami DumbLoc: dumb indoor localization framework using WiFi fingerprinting |
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Extant indoor positioning techniques based on received signal strength (RSS) fingerprinting have achieved accurate positioning results. They leverage existing open-source datasets for training and comparing their model performance. However, the models trained on one building cannot be used for another since these models learn the relationship of a specific set of RSSs to the building and floor locations necessitating the expensive, time-consuming process of fingerprinting. Even when we consider the individual datasets producing these excellent results, a lot of painstaking optimization is required which precludes a lot of people trying to implement indoor positioning quickly. Most of the input RSS vector is empty with redundant information and the static class labels used for buildings and floors make the models unusable on other buildings. We propose a machine learning-based framework that uses RSS values from the strongest access point (AP) signals and normalized output labels to combat this issue. The framework was used on the open-source UJI dataset using less than 5% of the 520 APs to achieve 94.15% and 8.45 m floor prediction accuracy and mean positioning error respectively without any optimization. This technique was reused on 10 other public datasets and achieved an average floor estimation accuracy of 91.93% when trained with new data and 88.68% without any new data compared to 87.1% of the closest competitor. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Narasimman, Srivathsan Chakaravarthi Alphones, Arokiaswami |
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Article |
author |
Narasimman, Srivathsan Chakaravarthi Alphones, Arokiaswami |
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Narasimman, Srivathsan Chakaravarthi |
title |
DumbLoc: dumb indoor localization framework using WiFi fingerprinting |
title_short |
DumbLoc: dumb indoor localization framework using WiFi fingerprinting |
title_full |
DumbLoc: dumb indoor localization framework using WiFi fingerprinting |
title_fullStr |
DumbLoc: dumb indoor localization framework using WiFi fingerprinting |
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DumbLoc: dumb indoor localization framework using WiFi fingerprinting |
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dumbloc: dumb indoor localization framework using wifi fingerprinting |
publishDate |
2024 |
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https://hdl.handle.net/10356/176574 |
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