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Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO

Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workf...

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Autores principales: Sun, Danshi, Wei, Erhu, Ma, Zhuoxi, Wu, Chenxi, Xu, Shiyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000105/
https://www.ncbi.nlm.nih.gov/pubmed/33808972
http://dx.doi.org/10.3390/s21061995
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author Sun, Danshi
Wei, Erhu
Ma, Zhuoxi
Wu, Chenxi
Xu, Shiyi
author_facet Sun, Danshi
Wei, Erhu
Ma, Zhuoxi
Wu, Chenxi
Xu, Shiyi
author_sort Sun, Danshi
collection PubMed
description Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods.
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spelling pubmed-80001052021-03-28 Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO Sun, Danshi Wei, Erhu Ma, Zhuoxi Wu, Chenxi Xu, Shiyi Sensors (Basel) Article Indoor navigation has attracted commercial developers and researchers in the last few decades. The development of localization tools, methods and frameworks enables current communication services and applications to be optimized by incorporating location data. For clinical applications such as workflow analysis, Bluetooth Low Energy (BLE) beacons have been employed to map the positions of individuals in indoor environments. To map locations, certain existing methods use the received signal strength indicator (RSSI). Devices need to be configured to allow for dynamic interference patterns when using the RSSI sensors to monitor indoor positions. In this paper, our objective is to explore an alternative method for monitoring a moving user’s indoor position using BLE sensors in complex indoor building environments. We developed a Convolutional Neural Network (CNN) based positioning model based on the 2D image composed of the received number of signals indicator from both x and y-axes. In this way, like a pixel, we interact with each 10 × 10 matrix holding the spatial information of coordinates and suggest the possible shift of a sensor, adding a sensor and removing a sensor. To develop CNN we adopted a neuro-evolution approach to optimize and create several layers in the network dynamically, through enhanced Particle Swarm Optimization (PSO). For the optimization of CNN, the global best solution obtained by PSO is directly given to the weights of each layer of CNN. In addition, we employed dynamic inertia weights in the PSO, instead of a constant inertia weight, to maintain the CNN layers’ length corresponding to the RSSI signals from BLE sensors. Experiments were conducted in a building environment where thirteen beacon devices had been installed in different locations to record coordinates. For evaluation comparison, we further adopted machine learning and deep learning algorithms for predicting a user’s location in an indoor environment. The experimental results indicate that the proposed optimized CNN-based method shows high accuracy (97.92% with 2.8% error) for tracking a moving user’s locations in a complex building without complex calibration as compared to other recent methods. MDPI 2021-03-12 /pmc/articles/PMC8000105/ /pubmed/33808972 http://dx.doi.org/10.3390/s21061995 Text en © 2021 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
Sun, Danshi
Wei, Erhu
Ma, Zhuoxi
Wu, Chenxi
Xu, Shiyi
Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO
title Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO
title_full Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO
title_fullStr Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO
title_full_unstemmed Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO
title_short Optimized CNNs to Indoor Localization through BLE Sensors Using Improved PSO
title_sort optimized cnns to indoor localization through ble sensors using improved pso
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8000105/
https://www.ncbi.nlm.nih.gov/pubmed/33808972
http://dx.doi.org/10.3390/s21061995
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