Abstract
In this paper we deal with implementation of outdoor positioning system based on WiFi network works in 5 GHz frequency band (IEEE802.11a). Positioning solution based on 5 GHz WiFi seems to be interesting, because the interference in this bandwidth with other networks is not so critical in comparison with 2.4 GHz WiFi. Positioning was based on fingerprinting method utilizing received signal strength information. The goal of the paper is to investigate an impact of different weather conditions during positioning process on positioning accuracy. Performance of the implemented positioning method was tested in two basic weather conditions, i.e. bad condition: raining and snowing, good condition: sunny. The experimental scenarios were implemented in the outdoor environment.
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References
Wu, C., Yang, Z., Liu, Y., Xi, W.: WILL: wireless indoor localization without site survey. In: INFOCOM 2012, pp. 64–72 (2012)
Ibrahim, M., Youssef, M.: Cell Sense: An Accurate Energy-Efficient GSM Positioning System. IEEE Trans. Vehicular Technology 61, 286–296 (2012)
Tokarcikova, E., Kucharcikova, A.: Diffusion of Innovation: The Case of the Slovak Mobile Communication Market. International Journal of Innovation and Learning 17(3), 359–370 (2015)
Cerny, M., Penhaker, M.: Wireless Body Sensor Network in Health Maintenance Systems. Journal Electronics and Electrical Engineering 115(9), 113–116 (2011)
Alzantot, M., Youssef, M.: UPTIME: ubiquitous pedestrian tracking using mobile phones. In: Wireless Communications and Networking Conference 2012 (WCNC), pp. 3204–3209 (2012)
Krejcar, O., Jirka, J., Janckulik, D.: Use of Mobile Phone as Intelligent Sensor for Sound Input Analysis and Sleep State Detection. Sensors 11(6), 6037–6055 (2011)
Bahl, P., Padmanabhan, V.N.: RADAR: an in–building RF–based user location and tracking system. In: IEEE Infocom 2000, vol. 2, pp. 775–784 (2000)
Krishnan, P., Krishnakumar, A.S., Wen-Hua, J., Mallows, C.: A aystem for LEASE: location estimation assisted by stationary emitters for indoor RF wireless networks. In: IEEE Infocom 2004, vol. 2. pp. 1181–1190 (2004)
Cipov, V., Dobos, L., Papaj, J.: Cooperative Trilateration-Based Positioning Algorithm for WLAN Nodes Using Round Trip Time Estimation. Journal of Electrical and Electronics Engineering. 4, 29–34 (2011)
Matic, A., Papliatseyeu, A., Osmani, V., Mayora-Ibarra, O.: Tuning to your position: FM-radio based indoor localization with spontaneous recalibration. In: IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 153–161 (2010)
Wang, X., Wang, Z., O’Dea, B.: A TOA-Based Location Algorithm Reducing the Errors Due to Non-line-of-sight (NLOS) Propagation. IEEE Tran. Vehicular Technology 52(2003), 112–116 (2003)
Haibo, Y., Tao, G., Xiaorui, Z., Jingwei, X., Xianping, T., Jian, L., Ning, J.: ftrack: infrastructure-free floor localization via mobile phone sensing. In: IEEE International Conference on Pervasive Computing and Communications (PerCom 2012), pp. 2–10 (2012)
Abdellatif, M., Mtibaa, A., Harras, K., Youssefy, M.: Greenloc: an energy efficient wifi localization on mobile phones. In: IEEE International Conference on ICC 2013 - Selected Areas in Communications Symposium, pp. 4425–4430 (2013)
Sen, S., Radunovic, B., Choudhury, R.R., Minka, T.: You are facing the mona lisa: spot localization using PHY layer information. In: 10th international conference on Mobile systems, applications, and services 2012, pp. 183–196 (2012)
Moghtadaiee, V., Dempster, A.: Indoor Location Fingerprinting Using FM Radio Signals. IEEE Trans. on Broadcasting 60(2), 336–346 (2014)
Kurner, T., Meier, A.: Prediction of Outdoor and Outdoor-to-Indoor Coverage in Urban Areas at 1.8 GHz. IEEE Journal on selected areas in communications 20(3), 496–506 (2002)
Laoudias, C., Piché, R., Panayiotou, C.G.: Device Self-Calibration in Location Systems Using Signal Strength Histograms. Journal of Location Based Services 7(3), 165–181 (2012)
Nerguizian, C., Despins, C., Affès, S.: Indoor geolocation with received signal strength fingerprinting technique and neural networks. In: de Souza, J.N., Dini, P., Lorenz, P. (eds.) ICT 2004. LNCS, vol. 3124, pp. 866–875. Springer, Heidelberg (2004)
Tsung-Nan, L., Po-Chiang, L.: Performance Comparison of Indoor Positioning Techniques Based on Location Fingerprinting in Wireless Networks. In International Conf. on Wireless Networks, Communications and Mobile. Computing 2, 1569–1574 (2005)
Kyklos, P.: Implementation of Localization System WifiLOC Based on IEEE802.11a for Outdoor Positioning. Diploma thesis, University of Zilina, p. 63 (2013)
Machaj, J., Brida, P., Piché, R.: Rank based fingerprinting algorithm for indoor positioning. In International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–6 (2011)
Brida, P., Machaj, J.: Mobile positioning solution suitable for intelligent transportation system based on IEEE 802.11a. In: New Trends in Software Methodologies, Tools and Techniques SOMET_2014, Langkawi, Malajzia, pp. 327–336 (2014)
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Brida, P., Machaj, J. (2015). Impact of Weather Conditions on Fingerprinting Localization Based on IEEE 802.11a. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_31
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DOI: https://doi.org/10.1007/978-3-319-24306-1_31
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