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A novel infrastructure WLAN locating method based on neural network

Published: 18 November 2008 Publication History

Abstract

Comparing to the client based locating, infrastructure based methods do not require install special software or hardware at client side. So it not only fits for real deployment, but also supports some specific requirement (ex target tracking). Former researchers mainly adopt k-NN method in infrastructure based locating. However, its computing complexity is proportional to the size of sample set, which makes it unscalable when the system grows large. This paper proposes a novel infrastructure WLAN locating method by utilizing neural networks and a new training method to overcome the effect of different power levels of client devices. Through real deployment and testing, the result shows that the computing complexity is much lower than the k-NN method, while the accuracy is very close.

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  • (2014)Indoor localization based on feed-forward Neural Networks and CIR fingerprinting techniques2014 IEEE Radio and Wireless Symposium (RWS)10.1109/RWS.2014.6830093(271-273)Online publication date: Jan-2014
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cover image ACM Conferences
AINTEC '08: Proceedings of the 4th Asian Conference on Internet Engineering
November 2008
144 pages
ISBN:9781605581279
DOI:10.1145/1503370
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 November 2008

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Author Tags

  1. WLAN
  2. infrastructure locating method
  3. neural network

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  • Research-article

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AINTEC 2008
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AINTEC 2008: Asian Internet Engineering Conference 2008
November 18 - 20, 2008
Bangkok, Pratunam, Thailand

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Overall Acceptance Rate 15 of 38 submissions, 39%

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  • (2021)A survey of deep learning approaches for WiFi-based indoor positioningJournal of Information and Telecommunication10.1080/24751839.2021.19754256:2(163-216)Online publication date: 20-Sep-2021
  • (2019)Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor LocalizationSensors10.3390/s1907167819:7(1678)Online publication date: 8-Apr-2019
  • (2014)Indoor localization based on feed-forward Neural Networks and CIR fingerprinting techniques2014 IEEE Radio and Wireless Symposium (RWS)10.1109/RWS.2014.6830093(271-273)Online publication date: Jan-2014
  • (2013)Fingerprint indoor positioning algorithm based on affinity propagation clusteringEURASIP Journal on Wireless Communications and Networking10.1186/1687-1499-2013-2722013:1Online publication date: 1-Dec-2013
  • (2013)RSS Fingerprints Based Distributed Semi-Supervised Locally Linear Embedding (DSSLLE) Location Estimation System for Indoor WLANWireless Personal Communications: An International Journal10.1007/s11277-012-0868-z71:2(1175-1192)Online publication date: 1-Jul-2013
  • (2013)Performance Analysis of Received Signal Strength Fingerprinting Based Distributed Location Estimation System for Indoor WLANWireless Personal Communications: An International Journal10.1007/s11277-012-0682-770:1(113-127)Online publication date: 1-May-2013
  • (2012)Distributed location estimation system using WLAN received signal strength fingerprints2012 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2012.6214338(3102-3106)Online publication date: Apr-2012
  • (2011)Smart spatio-temporal fingerprinting for cooperative ANN-based wireless localization in underground narrow-vein minesProceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies10.1145/2093698.2093870(1-5)Online publication date: 26-Oct-2011
  • (2011)Radio-localization in underground narrow-vein mines using neural networks with in-built tracking and time diversity2011 IEEE Wireless Communications and Networking Conference10.1109/WCNC.2011.5779404(1788-1793)Online publication date: Mar-2011
  • (2011)Cooperative geo-location in underground mines: A novel fingerprint positioning technique exploiting spatio-temporal diversity2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications10.1109/PIMRC.2011.6139715(1319-1324)Online publication date: Sep-2011
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