Multi-Floor Semantically Meaningful Localization Using IEEE 802.11 Network Beacons

Uzair AHMAD; Brian J. D'AURIOL; Young-Koo LEE; Sungyoung LEE

doi:10.1093/ietcom/e91-b.11.3450

Multi-Floor Semantically Meaningful Localization Using IEEE 802.11 Network Beacons

Uzair AHMAD, Brian J. D'AURIOL, Young-Koo LEE, Sungyoung LEE

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This paper presents a new methodology, Beacognition, for real-time discovery of the associations between a signal space and arbitrarily defined regions, termed as Semantically Meaningful Areas (SMAs), in the corresponding physical space. It lets the end users develop semantically meaningful location systems using standard 802.11 network beacons as they roam through their environment. The key idea is to discover the unique associations using a beacon popularity model. The popularity measurements are then used to localize the mobile devices. The beacon popularity is computed using an election' algorithm and a new recognition model is presented to perform the localization task. We have implemented such a location system in a five story campus building. The comparative results show significant improvement in localization by achieving on average 83% SMA and 88% Floor recognition rate in less than one minute per SMA training time.

Publication: IEICE TRANSACTIONS on Communications Vol.E91-B No.11 pp.3450-3460

Publication Date: 2008/11/01

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Online ISSN: 1745-1345

DOI: 10.1093/ietcom/e91-b.11.3450

Type of Manuscript: Special Section PAPER (Special Section on Emerging Technologies for Practical Ubiquitous and Sensor Networks)

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Uzair AHMAD, Brian J. D'AURIOL, Young-Koo LEE, Sungyoung LEE, "Multi-Floor Semantically Meaningful Localization Using IEEE 802.11 Network Beacons" in IEICE TRANSACTIONS on Communications, vol. E91-B, no. 11, pp. 3450-3460, November 2008, doi: 10.1093/ietcom/e91-b.11.3450.
Abstract: This paper presents a new methodology, Beacognition, for real-time discovery of the associations between a signal space and arbitrarily defined regions, termed as Semantically Meaningful Areas (SMAs), in the corresponding physical space. It lets the end users develop semantically meaningful location systems using standard 802.11 network beacons as they roam through their environment. The key idea is to discover the unique associations using a beacon popularity model. The popularity measurements are then used to localize the mobile devices. The beacon popularity is computed using an election' algorithm and a new recognition model is presented to perform the localization task. We have implemented such a location system in a five story campus building. The comparative results show significant improvement in localization by achieving on average 83% SMA and 88% Floor recognition rate in less than one minute per SMA training time.
URL: https://global.ieice.org/en_transactions/communications/10.1093/ietcom/e91-b.11.3450/_p

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@ARTICLE{e91-b_11_3450,
author={Uzair AHMAD, Brian J. D'AURIOL, Young-Koo LEE, Sungyoung LEE, },
journal={IEICE TRANSACTIONS on Communications},
title={Multi-Floor Semantically Meaningful Localization Using IEEE 802.11 Network Beacons},
year={2008},
volume={E91-B},
number={11},
pages={3450-3460},
abstract={This paper presents a new methodology, Beacognition, for real-time discovery of the associations between a signal space and arbitrarily defined regions, termed as Semantically Meaningful Areas (SMAs), in the corresponding physical space. It lets the end users develop semantically meaningful location systems using standard 802.11 network beacons as they roam through their environment. The key idea is to discover the unique associations using a beacon popularity model. The popularity measurements are then used to localize the mobile devices. The beacon popularity is computed using an election' algorithm and a new recognition model is presented to perform the localization task. We have implemented such a location system in a five story campus building. The comparative results show significant improvement in localization by achieving on average 83% SMA and 88% Floor recognition rate in less than one minute per SMA training time.},
keywords={},
doi={10.1093/ietcom/e91-b.11.3450},
ISSN={1745-1345},
month={November},}

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TY - JOUR
TI - Multi-Floor Semantically Meaningful Localization Using IEEE 802.11 Network Beacons
T2 - IEICE TRANSACTIONS on Communications
SP - 3450
EP - 3460
AU - Uzair AHMAD
AU - Brian J. D'AURIOL
AU - Young-Koo LEE
AU - Sungyoung LEE
PY - 2008
DO - 10.1093/ietcom/e91-b.11.3450
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E91-B
IS - 11
JA - IEICE TRANSACTIONS on Communications
Y1 - November 2008
AB - This paper presents a new methodology, Beacognition, for real-time discovery of the associations between a signal space and arbitrarily defined regions, termed as Semantically Meaningful Areas (SMAs), in the corresponding physical space. It lets the end users develop semantically meaningful location systems using standard 802.11 network beacons as they roam through their environment. The key idea is to discover the unique associations using a beacon popularity model. The popularity measurements are then used to localize the mobile devices. The beacon popularity is computed using an election' algorithm and a new recognition model is presented to perform the localization task. We have implemented such a location system in a five story campus building. The comparative results show significant improvement in localization by achieving on average 83% SMA and 88% Floor recognition rate in less than one minute per SMA training time.
ER -