This paper deals with the issue of mobile device localization in the environment of buildings, which is suitable for use in healthcare or crisis management. The developed localization system is based on wireless Local Area Network (LAN) infrastructure (commonly referred to as Wi-Fi), evaluating signal strength from different access points, using the fingerprinting method for localization. The most serious problems consist in multipath signal propagation and the different sensitivities (calibration) of Wi-Fi adapters installed in different mobile devices. To solve these issues, an algorithm based on fuzzy logic is proposed to optimize the localization performance. The localization system consists of five elements, which are mobile applications for Android OS, a fuzzy derivation model, and a web surveillance environment for displaying the localization results. All of these elements use a database and shared storage on a virtualized server running Ubuntu. The developed system is implemented in Java for Android-based mobile devices and successfully tested. The average accuracy is satisfactory for determining the position of a client device on the level of rooms.

Department of Economics and Control Systems VSB Technical University of Ostrava 17 listopadu 2172 15 708 00 Ostrava Poruba Czech Republic

Department of Telecommunications Engineering Czech Technical University Prague Technická 2 166 27 Praha 6 Czech Republic

Zobrazit více v PubMed

Hrad J., Vojtech L., Cihlar M., Stasa P., Neruda M., Svub J., Benes F. Fuzzy Logic Based Indoor Localization Using WLAN Infrastructure; Proceedings of the IEEE Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS); Tainan, Taiwan. 29–31 May 2020.

Konecny J., Urbanczyk T., Benes F., Stasa P. Framework AutoId Healthcare. Centre for Applied Cybernetics; Ostrava, Czech Republic: 2019.

Malik A. RTLS for Dummies. 1st ed. Wiley & Sons; New York, NY, USA: 2009.

Montaser A., Moselhi O. RFID indoor location identification for construction projects. Autom. Constr. 2014;39:167–179. doi: 10.1016/j.autcon.2013.06.012. DOI

Gu Y., Lo A., Niemegeers I. A survey of indoor positioning systems for wireless personal networks. IEEE Commun. Surv. Tutor. 2009;11:13–32. doi: 10.1109/SURV.2009.090103. DOI

Ding G., Zhang J., Zhang L., Tan Z. Overview of Received Signal Strength Based Fingerprinting Localization in Indoor Wireless LAN Environments; Proceedings of the 5th IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE); Chengdu, China. 29–31 October 2013; pp. 160–164. DOI

Chen Y., Crespi N., Lv L., Li M., Ortiz A.M., Shu L. Locating using prior information: Wireless indoor localization algorithm. Comput. Commun. Rev. 2013;43:463–464. doi: 10.1145/2534169.2491688. DOI

Wu C., Yang Z., Liu Y., Xi W. WILL: Wireless Indoor Localization without Site Survey; Proceedings of the IEEE International Conference on Computer Communications (INFOCOM); Orlando, FL, USA. 25–30 March 2012; pp. 64–72. DOI

Yang S., Dessai P., Verma M., Gerla M. FreeLoc: Calibration-Free Crowdsourced Indoor Localization; Proceedings of the IEEE International Conference on Computer Communications (INFOCOM); Turin, Italy. 14–19 April 2013; pp. 2481–2489. DOI

Farshad A., Li J., Marina M.K., Garcia F.J. A Microscopic Look at Wi-Fi Fingerprinting for Indoor Mobile Phone Localization in Diverse Environments; Proceedings of the 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN); Montbeliard-Belfort, France. 28–31 October 2013; DOI

Torres-Sospedra J., Jiménez A.R., Moreira A., Lungenstrass T., Lu W.-C., Knauth S., Mendoza-Silva G.M., Seco F., Pérez-Navarro A., Nicolau M.J., et al. Off-Line Evaluation of Mobile-Centric Indoor Positioning Systems: The Experiences from the 2017 IPIN Competition. Sensors. 2018;18:487. doi: 10.3390/s18020487. PubMed DOI PMC

Ferreira A.G., Fernandes D., Catarino A.P., Monteiro J. Performance Analysis of ToA-Based Positioning Algorithms for Static and Dynamic Targets with Low Ranging Measurements. Sensors. 2017;17:1915. doi: 10.3390/s17081915. PubMed DOI PMC

Honkavirta V., Perälä T., Ali-Löytty S., Piché R.A. Comparative Survey of WLAN Location Fingerprinting Methods; Proceedings of the 6th Workshop on Positioning, Navigation and Communication (WPNC); Hannover, Germany. 19 March 2009; pp. 243–251. DOI

Han D., Jung S., Lee M., Yoon G. Building a Practical Wi-Fi-Based Indoor Navigation System. IEEE Pervasive Comput. 2014;13:72–79. doi: 10.1109/mprv.2014.24. DOI

Ferreira A.G., Fernandes D., Catarino A., Rocha A.M., Monteiro J.L. A Loose-Coupled Fusion of Inertial and UWB Assisted by a Decision-Making Algorithm for Localization of Emergency Responders. Electronics. 2019;8:1463. doi: 10.3390/electronics8121463. DOI

Niu J., Gu Y., Lu B., Cheng L., Jun J.H. Wi-Fi Fingerprint Localization in Open Space. Lang. Cognit. Neurosci. 2013;2013:1–4.

He S., Chan S.-H.G. Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Commun. Surv. Tutor. 2015;18:466–490. doi: 10.1109/COMST.2015.2464084. DOI

Fuchs C., Aschenbruck N., Martini P., Wieneke M. Indoor tracking for mission critical scenarios: A survey. Pervasive Mob. Comput. 2011;7:1–15. doi: 10.1016/j.pmcj.2010.07.001. DOI

Fischer C., Gellersen H. Location and Navigation Support for Emergency Responders: A Survey. IEEE Pervasive Comput. 2009;9:38–47. doi: 10.1109/MPRV.2009.91. DOI

Ferreira A.G., Fernandes D., Catarino A., Monteiro J. Localization and Positioning Systems for Emergency Responders: A Survey. IEEE Commun. Surv. Tutor. 2017;19:2836–2870. doi: 10.1109/COMST.2017.2703620. DOI

Cihlář M. Bachelor’s Thesis. Czech Technical University; Prague, Czech Republic: 2014. Analýza Pokrytí Zadaného území Signálem pro Technologii RTLS v pásmu 2,45 GHz.

Hossain A.K.M., Jin Y., Soh W.-S., Van H.N. SSD: A Robust RF Location Fingerprint Addressing Mobile Devices’ Heterogeneity. IEEE Trans. Mob. Comput. 2011;12:65–77. doi: 10.1109/TMC.2011.243. DOI

Welch G., Bishop G. An Introduction to the Kalman Filter. University of North Carolina; Chapel Hill, NC, USA: 1995.

Wang L.-X., Mendel J.M. Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 1992;22:1414–1427. doi: 10.1109/21.199466. DOI

Casillas J., Cordón O., Herrera F. COR: A methodology to improve ad hoc data-driven linguistic rule learning methods by inducing cooperation among rules. IEEE Trans. Syst. Man Cybern. Part B. 2002;32:526–537. doi: 10.1109/TSMCB.2002.1018771. PubMed DOI

Yanar T.A., Akyürek Z. Fuzzy model tuning using simulated annealing. Expert Syst. Appl. 2011;38:8159–8169. doi: 10.1016/j.eswa.2010.12.159. DOI

Liu G., Yang W. Learning and Tuning of Fuzzy Membership Functions by Simulated Annealing Algorithm; Proceedings of the IEEE Asia-Pacific Conference on Circuits and Systems: Electronic Communication Systems; Tianjin, China. 4–6 December 2000; pp. 367–370.

Černý V. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. J. Optim. Theory Appl. 1985;45:41–51. doi: 10.1007/BF00940812. DOI

Kirkpatrick S., Gelatt C.D., Jr., Vecchi M.P. Optimization by Simulated Annealing. Science. 1983;220:671–680. doi: 10.1126/science.220.4598.671. PubMed DOI

Jorgensen W.L. Perspective on Equation of state calculations by fast computing machines. Theor. Chem. Acc. 2000;103:225–227. doi: 10.1007/s002149900053. DOI