research-article

A novel infrastructure WLAN locating method based on neural network

Authors:

Jianping WuAuthors Info & Claims

AINTEC '08: Proceedings of the 4th Asian Conference on Internet Engineering

Pages 47 - 55

https://doi.org/10.1145/1503370.1503385

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.

References

[1]

A S Krishnakumar and P Krishnan. The theory and practice of signal strength-based location estimation. In Collaborative Computing: Networking, Applications and Worksharing, 2005 International Conference on, pages 19--21, December 2005.

[2]

M D Rodriguez, J Favela, E A Martinez, and M A Munoz. Location-aware access to hospital information and services information technology in biomedicine. IEEE Transactions on, 8(4):448--455, December 2004.

Digital Library

[3]

R Battiti, A Villani, and T Le Nhat. Neural network models for intelligent networks: deriving the location from signal patterns. In Proceedings of AINS2002, 2002.

[4]

EA Martinez, A Cruz, and J Favela. Estimating user location in a wlan using backpropagation neural networks. In PIBERAMIA 2004 LNAI 3315, pages 737--746, November 2004.

[5]

U Ahmad, A Gavrilov, U Nasir, M Iqbal, Seong Jin Cho, and Sungyoung Lee. In-building localization using neural networks. In Engineering of Intelligent Systems, 2006 IEEE International Conference on, pages 1--6, 2006.

[6]

M Stella, M Russo, and D Begusic. Location determination in indoor environment based on rss fingerprinting and artificial neural network. In Eelecommunications, 2007. ConTel 2007. 9th International Conference on, pages 301--306, June 2007.

[7]

P Krishnan, A S Krishnakumar, Wen-Hua Ju, C Mallows, and S N Gamt. A system for lease: location estimation assisted by stationary emitters for indoor rf wireless networks. In INFOCOM 2004 Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, pages 100--101, March 2004.

[8]

S Gami, A S Krishnakumar, and P Krishnan. Infrastructure-based location estimation in wlan. In Wireless Communications and Networking Conference, 2004. WCNC. 2004 IEEE, pages 465--470, March 2004.

[9]

M Brunato and R Battiti. Statistical learning theory for location fingerprinting in wireless lans. Computer Networks, 47(6):825--845, April 2005.

Digital Library

[10]

Tsung-Nan Lin and Po-Chiang Lin. Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks. In Wireless Networks, Communications and Mobile Computing, 2005 International Conference on, pages 1569--1574, June 2005.

[11]

G I Wassi, C Despins, D Grenier, and C Nerguizian. Indoor location using received signal strength of ieee 802.11b access point. In Electrical and Computer Engineering, 2005. Canadian Conference on, pages 1367--1370, May 2005.

[12]

Hui Liu, H Darabi, P Banerjee, and Jing Liu. Survey of wireless indoor positioning techniques and systems. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 37(6):1067--1080, November 2007.

Digital Library

[13]

M Ciurana, FBarcelo-Arroyo, and S Cugno. A novel toa-based indoor tracking system over ieee 802.11 networks. In Mobile and Wireless Communications Summit, 2007. 16th IST, pages 1--5, July 2007.

[14]

D Niculescu and B Nath. Ad hoc positioning system (aps) using aoa. In In Proc. IEEE INFOCOM San Francisco, CA, pages 1734--1743, April 2003.

[15]

P Bahl and V N Padmanabhan. Radar: An in-building rf-based user location and tracking system. In Proceedings of the IEEE Infocom 2000, pages 775--784, March 2000.

[16]

D Madigan, E Einahrawy, R P Martin, W-H Ju, P Krishnan, and A S Krishnakumar. Bayesian indoor positioning systems. In INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE, pages 1217--1227, March 2005.

[17]

R Want, A Hopper, V Falcao, and J Gibbons. The active badge location system. ACM Transactions on Information Systems, 10:91--102, January 1992.

Digital Library

[18]

L M Ni, Y Liu, Y C Lau, and A P Patil. Landmarc: Indoor location sensing using active rfid. Wireless Netw, 10(6):701C710, November 2004.

Digital Library

[19]

Paul Castro, Patrick Chiu, Ted Kremenek, and Richard Muntz. A probabilistic location service for wireless network environments. In Proc. 3rd Int. Conf. Ubiquitous Comput., Atlanta, GA, page 18C34, September 2001.

Digital Library

[20]

P Bahl, A Balachandran, and V N Padmanabhan. Enhancements to the radar user location and tracking system. Microsoft Research Technical Report, February 2000.

Cited By

Feng XNguyen KLuo Z(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
https://doi.org/10.1080/24751839.2021.1975425
Salamah ATamazin MSharkas MKhedr MMahmoud M(2019)Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor LocalizationSensors10.3390/s1907167819:7(1678)Online publication date: 8-Apr-2019
https://doi.org/10.3390/s19071678
Zouari RZayani RBouallegue R(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
https://doi.org/10.1109/RWS.2014.6830093
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A novel infrastructure WLAN locating method based on neural network

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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

General Chair:
Kanchana Kanchanasut
Asian Institute of Technology, Thailand

Copyright © 2008 ACM.

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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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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Cited By

Feng XNguyen KLuo Z(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
https://doi.org/10.1080/24751839.2021.1975425
Salamah ATamazin MSharkas MKhedr MMahmoud M(2019)Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor LocalizationSensors10.3390/s1907167819:7(1678)Online publication date: 8-Apr-2019
https://doi.org/10.3390/s19071678
Zouari RZayani RBouallegue R(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
https://doi.org/10.1109/RWS.2014.6830093
Tian ZTang XZhou MTan Z(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
https://doi.org/10.1186/1687-1499-2013-272
Jain VTapaswi SShukla A(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
https://dl.acm.org/doi/10.1007/s11277-012-0868-z
Jain VTapaswi SShukla A(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
https://dl.acm.org/doi/10.1007/s11277-012-0682-7
Jain VTapaswi SShukla A(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
https://doi.org/10.1109/WCNC.2012.6214338
Dayekh SAffes SKandil NNerguizian CFrattasi SMarchetti N(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
https://dl.acm.org/doi/10.1145/2093698.2093870
Dayekh SAffes SKandil NNerguizian C(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
https://doi.org/10.1109/WCNC.2011.5779404
Dayekh SAffes SKandil NNerguizian C(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
https://doi.org/10.1109/PIMRC.2011.6139715
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