Abstract
Indoor Positioning Systems are more and more attractive research area and popular studies. They provide direct access of instant location information of people in large, complex locations such as airports, museums, hospitals, etc. Especially for elders and children, location information can be lifesaving in such complex places. Thanks to the smart technology that can be worn, daily accessories such as wristbands, smart clocks are suitable for this job. In this study, the earth’s magnetic field data is used to find location of devices. Having less noise rather than other type of data, magnetic field data provides high success. In this study, with this data, a positioning model is constructed by using Artificial Neural Network (ANN). Support Vector Machines(SVM) was used to compare the results of the model with the ANN. Also the accuracy of this model is calculated and how the number of hidden layer of neural network affects the accuracy is analyzed. Results show that magnetic field indoor positioning system accuracy can reach 95% with ANN.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pirzada, N., et al.: Comparative analysis of active and passive indoor localization systems. AASRI Procedia 5, 92–97 (2013)
Seco, F., et al.: A survey of mathematical methods for indoor localization. In: IEEE International Symposium on Intelligent Signal Processing, WISP 2009. IEEE (2009)
Galván-Tejada, C.E., et al.: Evaluation of four classifiers as cost function for indoor location systems. Procedia Comput. Sci. 32, 453–460 (2014)
Hightower, J., Borriello, G.: Location sensing techniques. IEEE Comput. 34(8), 57–66 (2001)
Wikimedia. https://commons.wikimedia.org/w/index.php?curid=19810392
National Centers for Environmental Information. https://www.ngdc.noaa.gov/geomag/models.shtml
Online Calculators for the World Magnetic Model. https://www.ngdc.noaa.gov/geomag/WMM/calculators.shtml
Angermann, M., et al.: Characterization of the indoor magnetic field for applications in localization and mapping. In: 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE (2012)
Ettlinger, A., Retscher, G.: Positioning using ambient magnetic fields in combination with Wi-Fi and RFID. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE (2016)
Bozkurt, S., et al.: A novel multi-sensor and multi-topological database for indoor positioning on fingerprint techniques. In: 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA). IEEE (2015)
Alpaydin, E. (2013). Yapay Öǧrenme. Boǧaziçi Üniversitesi Yayınevi. ISBN-13: 978-6-054-23849-1.18. Lin., C.-C.C.-J. (2001)
LIBSVM: a library for support vector machine. http://www.csie.ntu.edu.tw/~cjlin/libsvm
Acknowledgments
This work is also a part of the Ph.D. thesis titled “Design of an Efficient User Localization System for Next Generation Wireless Networks” at Istanbul University, Institute of Physical Sciences.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ustebay, S., Yiner, Z., Aydin, M.A., Sertbas, A., Atmaca, T. (2017). An Approach for Evaluating Performance of Magnetic-Field Based Indoor Positioning Systems: Neural Network. In: Gaj, P., Kwiecień, A., Sawicki, M. (eds) Computer Networks. CN 2017. Communications in Computer and Information Science, vol 718. Springer, Cham. https://doi.org/10.1007/978-3-319-59767-6_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-59767-6_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59766-9
Online ISBN: 978-3-319-59767-6
eBook Packages: Computer ScienceComputer Science (R0)