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Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence

One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human bod...

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Detalles Bibliográficos
Autores principales: Naghdi, Sharareh, O’Keefe, Kyle
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085643/
https://www.ncbi.nlm.nih.gov/pubmed/32121466
http://dx.doi.org/10.3390/s20051350
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author Naghdi, Sharareh
O’Keefe, Kyle
author_facet Naghdi, Sharareh
O’Keefe, Kyle
author_sort Naghdi, Sharareh
collection PubMed
description One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies.
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spelling pubmed-70856432020-04-21 Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence Naghdi, Sharareh O’Keefe, Kyle Sensors (Basel) Article One of the popular candidates in wireless technology for indoor positioning is Bluetooth Low Energy (BLE). However, this technology faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations due to the behavior of the different advertising channels and the effect of human body shadowing among other effects. In order to mitigate these effects, the paper proposes and implements a dynamic Artificial Intelligence (AI) model that uses the three different BLE advertising channels to detect human body shadowing and compensate the RSSI values accordingly. An experiment in an indoor office environment is conducted. 70% of the observations are randomly selected and used for training and the remaining 30% are used to evaluate the algorithm. The results show that the AI model can properly detect and significantly compensate RSSI values for a dynamic blockage caused by a human body. This can significantly improve the RSSI-based ranges and the corresponding positioning accuracies. MDPI 2020-02-29 /pmc/articles/PMC7085643/ /pubmed/32121466 http://dx.doi.org/10.3390/s20051350 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 Article
Naghdi, Sharareh
O’Keefe, Kyle
Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
title Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
title_full Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
title_fullStr Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
title_full_unstemmed Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
title_short Detecting and Correcting for Human Obstacles in BLE Trilateration Using Artificial Intelligence
title_sort detecting and correcting for human obstacles in ble trilateration using artificial intelligence
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085643/
https://www.ncbi.nlm.nih.gov/pubmed/32121466
http://dx.doi.org/10.3390/s20051350
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