Cargando…
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...
Autores principales: | , |
---|---|
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 |
_version_ | 1783508978999754752 |
---|---|
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. |
format | Online Article Text |
id | pubmed-7085643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT naghdisharareh detectingandcorrectingforhumanobstaclesinbletrilaterationusingartificialintelligence AT okeefekyle detectingandcorrectingforhumanobstaclesinbletrilaterationusingartificialintelligence |