Ghent University Academic Bibliography

Advanced

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Frank Vanheel UGent, Jo Verhaevert UGent, Eric Laermans UGent, Ingrid Moerman UGent and Piet Demeester UGent (2011) EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING.
abstract
Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
correlation and regression analysis, algorithm design and analysis, SYSTEMS, ALGORITHM, WIRELESS NETWORKS, FUNDAMENTAL LIMITS, WIDE-BAND LOCALIZATION, localization, wireless sensor networks
journal title
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING
EURASIP J. Wirel. Commun. Netw.
article_number
38
pages
27 pages
Web of Science type
Article
Web of Science id
000294917000001
ISSN
1687-1472
DOI
10.1186/1687-1499-2011-38
language
English
UGent publication?
yes
classification
A1
copyright statement
I have retained and own the full copyright for this publication
id
1948259
handle
http://hdl.handle.net/1854/LU-1948259
date created
2011-11-23 15:45:10
date last changed
2012-02-06 10:11:28
@article{1948259,
  abstract     = {Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.},
  articleno    = {38},
  author       = {Vanheel, Frank and Verhaevert, Jo and Laermans, Eric and Moerman, Ingrid and Demeester, Piet},
  issn         = {1687-1472},
  journal      = {EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING},
  keyword      = {correlation and regression analysis,algorithm design and analysis,SYSTEMS,ALGORITHM,WIRELESS NETWORKS,FUNDAMENTAL LIMITS,WIDE-BAND LOCALIZATION,localization,wireless sensor networks},
  language     = {eng},
  pages        = {27},
  title        = {Automated linear regression tools improve RSSI WSN localization in multipath indoor environment},
  url          = {http://dx.doi.org/10.1186/1687-1499-2011-38},
  year         = {2011},
}

Chicago
Vanheel, Frank, Jo Verhaevert, Eric Laermans, Ingrid Moerman, and Piet Demeester. 2011. “Automated Linear Regression Tools Improve RSSI WSN Localization in Multipath Indoor Environment.” Eurasip Journal on Wireless Communications and Networking.
APA
Vanheel, F., Verhaevert, J., Laermans, E., Moerman, I., & Demeester, P. (2011). Automated linear regression tools improve RSSI WSN localization in multipath indoor environment. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING.
Vancouver
1.
Vanheel F, Verhaevert J, Laermans E, Moerman I, Demeester P. Automated linear regression tools improve RSSI WSN localization in multipath indoor environment. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING. 2011;
MLA
Vanheel, Frank, Jo Verhaevert, Eric Laermans, et al. “Automated Linear Regression Tools Improve RSSI WSN Localization in Multipath Indoor Environment.” EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING (2011): n. pag. Print.