A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning
Impacto
![Google Scholar](/xmlui/themes/Mirage2/images/uji/logo_google.png)
![Microsoft Academico](/xmlui/themes/Mirage2/images/uji/logo_microsoft.png)
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
comunitat-uji-handle4:
INVESTIGACIONEste recurso está restringido
https://doi.org/10.1109/IJCNN55064.2022.9892484 |
Metadatos
Título
A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor PositioningFecha de publicación
2022Editor
IEEECita bibliográfica
P. Pascacio, J. Torres–Sospedra, S. Casteleyn and E. S. Lohan, "A Collaborative Approach Using Neural Networks for BLE-RSS Lateration-Based Indoor Positioning," 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 01-09, doi: 10.1109/IJCNN55064.2022.9892484.Tipo de documento
info:eu-repo/semantics/conferenceObjectVersión de la editorial
https://ieeexplore.ieee.org/document/9892484Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
In daily life, mobile and wearable devices with high
computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands
for indoor location-based services. ... [+]
In daily life, mobile and wearable devices with high
computing power, together with anchors deployed in indoor environments, form a common solution for the increasing demands
for indoor location-based services. Within the technologies and
methods currently in use for indoor localization, the approaches
that rely on Bluetooth Low Energy (BLE) anchors, Received
Signal Strength (RSS), and lateration are among the most
popular, mainly because of their cheap and easy deployment
and accessible infrastructure by a variety of devices. Nevertheless, such BLE- and RSS-based indoor positioning systems
are prone to inaccuracies, mostly due to signal fluctuations,
poor quantity of anchors deployed in the environment, and/or
inappropriate anchor distributions, as well as mobile device
hardware variability. In this paper, we address these issues
by using a collaborative indoor positioning approach, which
exploits neighboring devices as additional anchors in an extended
positioning network. The collaborating devices’ information (i.e.,
estimated positions and BLE-RSS) is processed using a multilayer
perceptron (MLP) neural network by taking into account the
device specificity in order to estimate the relative distances.
After this, the lateration is applied to collaboratively estimate
the device position. Finally, the stand-alone and collaborative
position estimates are combined, providing the final position
estimate for each device. The experimental results demonstrate
that the proposed collaborative approach outperforms the standalone lateration method in terms of positioning accuracy. [-]
Descripción
Ponencia presentada en: 2022 International Joint Conference on Neural Networks (IJCNN), 18-23 July 2022.
Publicado en
2022 International Joint Conference on Neural Networks (IJCNN). IEEE, 2022. p. 01-09Entidad financiadora
European Commission
Código del proyecto o subvención
813278 | 101023072
Derechos de acceso
Aparece en las colecciones
- INIT_Articles [750]