Next Article in Journal
Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches
Previous Article in Journal
Segmentation of White Blood Cells through Nucleus Mark Watershed Operations and Mean Shift Clustering
Article

Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks

Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n., Col. Las Campanas, Santiago de Querétaro 76010, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Sensors 2015, 15(9), 22587-22615; https://doi.org/10.3390/s150922587
Received: 14 June 2015 / Accepted: 25 August 2015 / Published: 8 September 2015
(This article belongs to the Section Sensor Networks)
Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate. View Full-Text
Keywords: sensor modeling; WLAN received signal strength; Gaussian process regression; machine learning; location fingerprinting sensor modeling; WLAN received signal strength; Gaussian process regression; machine learning; location fingerprinting
Show Figures

Graphical abstract

MDPI and ACS Style

Richter, P.; Toledano-Ayala, M. Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks. Sensors 2015, 15, 22587-22615. https://doi.org/10.3390/s150922587

AMA Style

Richter P, Toledano-Ayala M. Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks. Sensors. 2015; 15(9):22587-22615. https://doi.org/10.3390/s150922587

Chicago/Turabian Style

Richter, Philipp, and Manuel Toledano-Ayala. 2015. "Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks" Sensors 15, no. 9: 22587-22615. https://doi.org/10.3390/s150922587

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop