Abstract
In this work we proposed an indoor location system that makes use of a mobile phone and WiFi signal levels to determine the location of a person in the museum “Eduardo de Habich” at the Universidad Nacional de Ingeniería, Peru. Therefore, by determining the location, additional information such as recommendations, multimedia, an more could be shown to each user in order to have a better user experience. The main advantage with similar indoor location systems such as Beacons or RFID technology is that the proposed system does not require additional hardware as it only uses pre-installed WiFi hotspots. The experimental tests show promising results, achieving a location accuracy of 93.61%, which is useful for similar navigation tasks.
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
Motte, H., Wyffels, J., De Strycker, L., Goemaere, J.-P.: Evaluating GPS data in indoor environments. Adv. Electr. Comput. Eng. 11(3), 25–28 (2011). https://doi.org/10.4316/AECE.2011.03004
Sakpere, W., Adeyeye Oshin, M., Mlitwa, N.B.: A state-of-the-art survey of indoor positioning and navigation systems and technologies. South Afr. Comput. J. 29(3), 145–197 (2017)
Zafari, F., Gkelias, A., Leung, K.: A survey of indoor localization systems and technologies. arXiv:1709.01015v3 (2019)
Brena, R.F., García-Vázquez, J.P., et al.: Evolution of indoor positioning technologies: a survey. J. Sens. 2017, 21 (2017)
Dhital, A., Closas, P., Fernández-Prades, C.: Bayesian filters for indoor localization using wireless sensor networks. In: 5th ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC). Noordwijk, vol. 2010, pp. 1–7 (2010)
Moreno-Cano, M.V., Zamora-Izquierdo, M.A., Santa, J., Skarmeta, A.F.: An indoor localization system based on artificial neural networks and particle filters applied to intelligent buildings. Neurocomput. 122, 116–125 (2013)
Li, X., Cao, Y., Chen, C.: Machine learning based high accuracy indoor visible light location algorithm. In: 2018 IEEE International Conference on Smart Internet of Things (SmartIoT), Xi’an, pp. 198–203 (2018). https://doi.org/10.1109/SmartIoT.2018.00043
Lin, K., Chen, M., Deng, J., Hassan, M.M., Fortino, G.: Enhanced fingerprinting and trajectory prediction for IoT localization in smart buildings. IEEE Trans. Autom. Sci. Eng. 13(3), 1294–1307 (2016)
Indoor Google Maps. http://maps.google.com/help/maps/indoormaps/. Accessed Jan 2014
Chruszczyk, L.: Statistical analysis of indoor RSSI read-outs for 433 MHz, 868 MHz, 2.4 GHz and 5 GHz ISM bands. Int. J. Electron. Telecommun. 63(1), 33–38 (2017)
Kaji, K., Kawaguchi, N.: Design and implementation of WiFi indoor localization based on Gaussian mixture model and particle filter. In: 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sydney, NSW, pp. 1–9 (2012)
Park, J.-G., et al.: Growing an organic indoor location system. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys 2010), pp. 271–284. ACM, New York (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Núñez-Fernández, D. (2020). Implementation of an Indoor Location System for Mobile-Based Museum Guidance. In: Lossio-Ventura, J.A., Condori-Fernandez, N., Valverde-Rebaza, J.C. (eds) Information Management and Big Data. SIMBig 2019. Communications in Computer and Information Science, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-46140-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-46140-9_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46139-3
Online ISBN: 978-3-030-46140-9
eBook Packages: Computer ScienceComputer Science (R0)