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LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion †

In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is per...

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Autores principales: Podevijn, Nico, Trogh, Jens, Aernouts, Michiel, Berkvens, Rafael, Martens, Luc, Weyn, Maarten, Joseph, Wout, Plets, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602372/
https://www.ncbi.nlm.nih.gov/pubmed/33066683
http://dx.doi.org/10.3390/s20205815
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author Podevijn, Nico
Trogh, Jens
Aernouts, Michiel
Berkvens, Rafael
Martens, Luc
Weyn, Maarten
Joseph, Wout
Plets, David
author_facet Podevijn, Nico
Trogh, Jens
Aernouts, Michiel
Berkvens, Rafael
Martens, Luc
Weyn, Maarten
Joseph, Wout
Plets, David
author_sort Podevijn, Nico
collection PubMed
description In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution.
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spelling pubmed-76023722020-11-01 LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion † Podevijn, Nico Trogh, Jens Aernouts, Michiel Berkvens, Rafael Martens, Luc Weyn, Maarten Joseph, Wout Plets, David Sensors (Basel) Letter In contrast to accurate GPS-based localization, approaches to localize within LoRaWAN networks offer the advantages of being low power and low cost. This targets a very different set of use cases and applications on the market where accuracy is not the main considered metric. The localization is performed by the Time Difference of Arrival (TDoA) method and provides discrete position estimates on a map. An accurate “tracking-on-demand” mode for retrieving lost and stolen assets is important. To enable this mode, we propose deploying an e-compass in the mobile LoRa node, which frequently communicates directional information via the payload of the LoRaWAN uplink messages. Fusing this additional information with raw TDoA estimates in a map matching algorithm enables us to estimate the node location with a much increased accuracy. It is shown that this sensor fusion technique outperforms raw TDoA at the cost of only embedding a low-cost e-compass. For driving, cycling, and walking trajectories, we obtained minimal improvements of 65, 76, and 82% on the median errors which were reduced from 206 to 68 m, 197 to 47 m, and 175 to 31 m, respectively. The energy impact of adding an e-compass is limited: energy consumption increases by only 10% compared to traditional LoRa localization, resulting in a solution that is still 14 times more energy-efficient than a GPS-over-LoRa solution. MDPI 2020-10-14 /pmc/articles/PMC7602372/ /pubmed/33066683 http://dx.doi.org/10.3390/s20205815 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Letter
Podevijn, Nico
Trogh, Jens
Aernouts, Michiel
Berkvens, Rafael
Martens, Luc
Weyn, Maarten
Joseph, Wout
Plets, David
LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion †
title LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion †
title_full LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion †
title_fullStr LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion †
title_full_unstemmed LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion †
title_short LoRaWAN Geo-Tracking Using Map Matching and Compass Sensor Fusion †
title_sort lorawan geo-tracking using map matching and compass sensor fusion †
topic Letter
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7602372/
https://www.ncbi.nlm.nih.gov/pubmed/33066683
http://dx.doi.org/10.3390/s20205815
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