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Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments

The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one o...

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Main Authors: Alshami, I. H., Ahmad, N. A., Sahibuddin, S., Firdaus, F.
Format: Article
Language:English
Published: MDPI AG 2017
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/75664/1/NoorAzuratiAhmad_AdaptiveIndoorPositioningModel.pdf
http://eprints.utm.my/id/eprint/75664/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026902726&doi=10.3390%2fs17081789&partnerID=40&md5=689e9c7d5232dd9c3b539fbb73524f4b
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spelling my.utm.756642018-04-27T01:42:46Z http://eprints.utm.my/id/eprint/75664/ Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments Alshami, I. H. Ahmad, N. A. Sahibuddin, S. Firdaus, F. QA75 Electronic computers. Computer science The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices. MDPI AG 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/75664/1/NoorAzuratiAhmad_AdaptiveIndoorPositioningModel.pdf Alshami, I. H. and Ahmad, N. A. and Sahibuddin, S. and Firdaus, F. (2017) Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments. Sensors (Switzerland), 17 (8). ISSN 1424-8220 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026902726&doi=10.3390%2fs17081789&partnerID=40&md5=689e9c7d5232dd9c3b539fbb73524f4b
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Alshami, I. H.
Ahmad, N. A.
Sahibuddin, S.
Firdaus, F.
Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments
description The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.
format Article
author Alshami, I. H.
Ahmad, N. A.
Sahibuddin, S.
Firdaus, F.
author_facet Alshami, I. H.
Ahmad, N. A.
Sahibuddin, S.
Firdaus, F.
author_sort Alshami, I. H.
title Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments
title_short Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments
title_full Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments
title_fullStr Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments
title_full_unstemmed Adaptive indoor positioning model based on WLAN-fingerprinting for dynamic and multi-floor environments
title_sort adaptive indoor positioning model based on wlan-fingerprinting for dynamic and multi-floor environments
publisher MDPI AG
publishDate 2017
url http://eprints.utm.my/id/eprint/75664/1/NoorAzuratiAhmad_AdaptiveIndoorPositioningModel.pdf
http://eprints.utm.my/id/eprint/75664/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026902726&doi=10.3390%2fs17081789&partnerID=40&md5=689e9c7d5232dd9c3b539fbb73524f4b
_version_ 1643657125548785664
score 13.211869

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