eprintid: 13118
rev_number: 6
eprint_status: archive
userid: 2
dir: disk0/00/01/31/18
datestamp: 2024-07-09 23:30:08
lastmod: 2024-07-09 23:30:09
status_changed: 2024-07-09 23:30:08
type: article
metadata_visibility: show
creators_name: Taneja, Ashu
creators_name: Rani, Shalli
creators_name: Breñosa, Jose
creators_name: Tolba, Amr
creators_name: Kadry, Seifedine
creators_id: 
creators_id: 
creators_id: josemanuel.brenosa@uneatlantico.es
creators_id: 
creators_id: 
title: An improved WiFi sensing based indoor navigation with reconfigurable intelligent surfaces for 6G enabled IoT network and AI explainable use case
ispublished: pub
subjects: uneat_eng
divisions: uneatlantico_produccion_cientifica
divisions: uninipr_produccion_cientifica
full_text_status: none
abstract: The expanding number of low cost sensors and smart devices drives the internet-of-things (IoT) ecosystem of the future. These sensing devices are connected to the internet for information exchange. The location and positioning of these nodes is very important information required in vast range of location based services like smart homes, smart healthcare, environmental monitoring, personal navigation and smart transportation. This paper presents an intelligent solution for node localization in a 6G enabled IoT network. An indoor communication network scenario is proposed in which reconfigurable intelligent surfaces (RISs) are installed to locate the sensor nodes operating in that network. The performance evaluation of the proposed scheme is carried out with optimum number of reflecting elements and optimum phase shifts. It is observed that optimized RISs with 100 reflecting elements improve the estimated localization error by 7.4% over non-optimum RISs. Also, the minimum gain of 6% in localization error is offered using equal phase shifts over random phase shifts. Further, the effect of channel conditions on the average estimation error in node locations is also elaborated. In the end, the explainable artificial intelligence (XAI) empowered indoor localization is discussed as a use case scenario and the performance comparison of the algorithms is evaluated.
date: 2023-12
publication: Future Generation Computer Systems
volume: 149
pagerange: 294-303
id_number: doi:10.1016/j.future.2023.07.016
refereed: TRUE
issn: 0167739X
official_url: http://doi.org/10.1016/j.future.2023.07.016
access: close
language: en
citation:   Artículo Materias > Ingeniería <http://repositorio.uneatlantico.es/view/subjects/uneat=5Feng.html> Universidad Europea del Atlántico > Investigación > Artículos y libros <http://repositorio.uneatlantico.es/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html>
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica <http://repositorio.uneatlantico.es/view/divisions/uninipr=5Fproduccion=5Fcientifica.html> Cerrado Inglés The expanding number of low cost sensors and smart devices drives the internet-of-things (IoT) ecosystem of the future. These sensing devices are connected to the internet for information exchange. The location and positioning of these nodes is very important information required in vast range of location based services like smart homes, smart healthcare, environmental monitoring, personal navigation and smart transportation. This paper presents an intelligent solution for node localization in a 6G enabled IoT network. An indoor communication network scenario is proposed in which reconfigurable intelligent surfaces (RISs) are installed to locate the sensor nodes operating in that network. The performance evaluation of the proposed scheme is carried out with optimum number of reflecting elements and optimum phase shifts. It is observed that optimized RISs with 100 reflecting elements improve the estimated localization error by 7.4% over non-optimum RISs. Also, the minimum gain of 6% in localization error is offered using equal phase shifts over random phase shifts. Further, the effect of channel conditions on the average estimation error in node locations is also elaborated. In the end, the explainable artificial intelligence (XAI) empowered indoor localization is discussed as a use case scenario and the performance comparison of the algorithms is evaluated. metadata Taneja, Ashu; Rani, Shalli; Breñosa, Jose; Tolba, Amr y Kadry, Seifedine mail SIN ESPECIFICAR, SIN ESPECIFICAR, josemanuel.brenosa@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR       (2023) An improved WiFi sensing based indoor navigation with reconfigurable intelligent surfaces for 6G enabled IoT network and AI explainable use case.  Future Generation Computer Systems, 149.  pp. 294-303.  ISSN 0167739X