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Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi

Year 2018, Volume: 22 Issue: Special, 367 - 374, 05.10.2018

Taner Arsan

Abstract

Düşük enerjili Bluetooth işaretçi (Bluetooth low energy - BLE beacon) teknolojisi, iç mekan konum belirleme sistemlerinde başarılı ve düşük maliyetli çözümler sunan gelişmekte olan bir teknolojidir. Bu çalışmada, BLE işaretçileri (beacons) kullanan bir iç mekan konum belirleme sistemi geliştirilmiş, kullanılan ilave algoritmalarla standart sensörlerden elde edilen konum değerlerinin doğruluğunun artırılması amaçlanmıştır. Bunun için, deneysel iç mekan konum algılama sisteminden elde edilen konum bilgilerine Büyük Patlama – Büyük Çöküş (Big Bang – Big Crunch (BB-BC)) optimizasyon yöntemi uygulanmış ve konum doğruluğunun geliştirildiği yapılan testlerle kanıtlanmıştır. Test alanı olarak, 9,60 m × 3,90 m boyutundaki 37,44 m2'lik alan seçilmiş ve 2,40 m × 1,30 m boyutundaki oniki tane ızgara alanına ayak izi (fingerprinting) algoritması uygulanmıştır. Test alanına dört tane BLE işaretçi (beacon) yerleştirilmiş, on iki test alanından 150 saniye boyunca toplam 9.000 ölçüm yapılmıştır. Ölçüm sonuçları Büyük Patlama – Büyük Çöküş optimizasyon yöntemi ile Öklid uzaklık eşleştirme yöntemi ve Kalman Filtresi kullanılarak iyileştirilmiş, bu sayede konum doğruluğu %26,62'den %75,69'a arttırılmıştır.

Keywords

Büyük Patlama – Büyük Çöküş optimizasyon yöntemi İç mekan konum belirleme; Konum doğruluğu; Düşük enerjili bluetooth işaretçi (beacon); Kalman filtresi

References

  • [1] Hofmann-Wellenhof, B., Lichtenegger, H., and Collins, J. 2001. Global positioning system: Theory and practice. Springer, Wien, Austria.
  • [2] Djuknic, G.M., Richton, R.E. 2001. Geolocation and assisted GPS. Computer, 34(2001), 123–125.
  • [3] Huang, H., Gartner, G. 2010. A Survey of Mobile Indoor Navigation Systems. Cartography in Central and Eastern Europe, Gartner G, Ortag F. Eds., Springer: Heidelberg, Germany, 305–319.
  • [4] Liu, H., Darabi, H., Banerjee, P., Liu, J. 2007. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(2007), 1067–1080.
  • [5] Ram, S., Sharf, J. 1998. The people sensor: A mobility aid for the visually impaired. In Proceedings of the Second International Symposium on Wearable Computers, Pittsburgh, PA, USA, 166–167.
  • [6] Gu, Y., Lo, A., Niemegeers, I. 2009. A survey of indoor positioning systems for wireless personal networks. Tutorial IEEE Communication Survey, 11, 13–32.
  • [7] Ganick, A., Ryan, D. 2012. Method and system for modulating a light source in a light based positioning system using a DC bias. US Patent 8,334,901.
  • [8] Komine, T., Nakagawa, M. 2004. Fundamental analysis for visiblelight communication system using LED lights. IEEE Transactions on Consumer Electronics, vol. 50, no. 1, 100–107.
  • [9] Kumar, N., Lourenco, N., Spiez M. et al., 2008. Visible light communication systems conception and vidas. IETE Technical Review, vol. 25, no. 6, article 359.
  • [10] Galvan-Tejada, C.E., Garcia-Vazquez, J.P. Brena, R.F. 2014. Magnetic field feature extraction and selection for indoor location estimation. Sensors, vol. 14, no. 6, 11001–11015.
  • [11] Shao, W., Zhao, F., Wang, C., Luo, H., Muhammad Zahid, T., Wang, Q., Li, D., 2016. Location Fingerprint Extraction for Magnetic Field Magnitude Based Indoor Positioning. Journal of Sensors 2016, 1–16.
  • [12] Ferreira, C.M.S., Oliveira, R.A.R., Gambini, H.S. and Frery, A.C. 2013. Characterization of FHSS in Wireless Personal Area Networks. 39–44.
  • [13] Kriz P, Maly F, Kozel T. 2016. Improving Indoor Localization Using Bluetooth Low Energy Beacons. Mobile Information Systems, 1–11.
  • [14] Dag, T., Arsan, T., 2017. Received signal strength based least squares lateration algorithm for indoor localization. Computers & Electrical Engineering (in Press).
  • [15] Xu, J., Ma, M., Law, C. 2008. AOA Cooperative Position Localization. In: Proceedings of the global telecommunications conference, IEEE GLOBECOM 2008, 1–5 .
  • [16] Lee, Y. 2011. Weighted-average based aoa parameter estimations for LR-UWB wireless positioning system. IEICE Transactions on Communications, 94, 3, 599–602 .
  • [17] Dardari, D., Conti, A., Ferner, U., Giorgetti, A., Win M.Z. 2009. Ranging with ultrawide bandwidth signals in multipath environments. IEEE Proceedings, 97, 404–26.
  • [18] Alsindi, N., Alavi, B., Pahlavan, K. 2007. Spatial characteristics of UWB TOAbased ranging in indoor multipath environments. In: Proceedings of the 18th IEEE international symposium on personal, indoor and mobile radio communications, Athens, Greece, 1–6.
  • [19] Alsindi, N.A., Alavi, B., Pahlavan, K. 2009. Measurement and modeling of ultra wide band TOA–based ranging in indoor multipath environments. IEEE Trans Veh Technologies, 58 (3), 1046 – 58.
  • [20] Liu, H., Darabi, H., Banerjee, P., Liu, J. 2007. Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybernet Part C, 37 (6), 1067 – 80.
  • [21] Gezici, S. 2008. A survey on wireless position estimation. Wireless Pers Communicaiton, 44 (3), 263 – 82.
  • [22] Samuel, H., Connell, S., Milligan, I., Austin, D., Hayes, T.L., Chiang, P. 2011. Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials. In: Proceedings of the 33rd annual international conference of the IEEE engineering in medicine and biology society (EMBS ’11), 7598–7601.
  • [23] Pai, D., Malpani, M., Sasi, I., Aggarwal, N., Mantripragada, P.S. 2012. A Robust pedestrian dead reckoning system on smartphones. In: Proceedings of the IEEE 11th international conference on trust, security and privacy in computing and communications (TrustCom ’12), 2000–2007.
  • [24] Bahl, P., Padmanabhan, V.N.2000. RADAR: an in-building RF-based user location and tracking system. IEEE Infocom 2000, Tel Aviv, Israel, 2, 775–84.
  • [25] Saha, S., Chaudhuri, K., Sanghi, D., Bhagwat, P. 2003. Location determination of a mobile device using IEEE 802.11b access point signals. In: IEEE wireless communications & networking conference (WCNC), vol. 3, 1987–92.
  • [26] Moghtadaiee, V., Dempster, A.G. 2015. Determining the best vector distance measure for use in location fingerprinting. Pervasive Mobile Computing, 23, 59–79.
  • [27] Erol, O.K., Eksin, I. 2006. A new optimization method: Big Bang – Big Crunch. Advances in Engineering Software, 37(2), 106–11.
  • [28] Chen, Z., Zou, H., Jiang, H., Zhu, Q., Soh, Y., Xie, L., 2015. Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization. Sensors 15, 715–732.
  • [29] Arsan, T., Kepez, O., 2017. Early Steps in Automated Behavior Mapping via Indoor Sensors. Sensors 17, 2925.
  • [30] Li, X., Wang, J. and Liu, C., 2015. “A Bluetooth/PDR Integration Algorithm for an Indoor Positioning System,” Sensors, vol. 15, no. 10, pp. 24862–24885
  • [31] Tuncer, T., 2017. “Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm”, International Journal of Computational Intelligence Systems, Vol. 10 (2017) 1056–1065.

Year 2018, Volume: 22 Issue: Special, 367 - 374, 05.10.2018

Taner Arsan

Abstract

References

  • [1] Hofmann-Wellenhof, B., Lichtenegger, H., and Collins, J. 2001. Global positioning system: Theory and practice. Springer, Wien, Austria.
  • [2] Djuknic, G.M., Richton, R.E. 2001. Geolocation and assisted GPS. Computer, 34(2001), 123–125.
  • [3] Huang, H., Gartner, G. 2010. A Survey of Mobile Indoor Navigation Systems. Cartography in Central and Eastern Europe, Gartner G, Ortag F. Eds., Springer: Heidelberg, Germany, 305–319.
  • [4] Liu, H., Darabi, H., Banerjee, P., Liu, J. 2007. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(2007), 1067–1080.
  • [5] Ram, S., Sharf, J. 1998. The people sensor: A mobility aid for the visually impaired. In Proceedings of the Second International Symposium on Wearable Computers, Pittsburgh, PA, USA, 166–167.
  • [6] Gu, Y., Lo, A., Niemegeers, I. 2009. A survey of indoor positioning systems for wireless personal networks. Tutorial IEEE Communication Survey, 11, 13–32.
  • [7] Ganick, A., Ryan, D. 2012. Method and system for modulating a light source in a light based positioning system using a DC bias. US Patent 8,334,901.
  • [8] Komine, T., Nakagawa, M. 2004. Fundamental analysis for visiblelight communication system using LED lights. IEEE Transactions on Consumer Electronics, vol. 50, no. 1, 100–107.
  • [9] Kumar, N., Lourenco, N., Spiez M. et al., 2008. Visible light communication systems conception and vidas. IETE Technical Review, vol. 25, no. 6, article 359.
  • [10] Galvan-Tejada, C.E., Garcia-Vazquez, J.P. Brena, R.F. 2014. Magnetic field feature extraction and selection for indoor location estimation. Sensors, vol. 14, no. 6, 11001–11015.
  • [11] Shao, W., Zhao, F., Wang, C., Luo, H., Muhammad Zahid, T., Wang, Q., Li, D., 2016. Location Fingerprint Extraction for Magnetic Field Magnitude Based Indoor Positioning. Journal of Sensors 2016, 1–16.
  • [12] Ferreira, C.M.S., Oliveira, R.A.R., Gambini, H.S. and Frery, A.C. 2013. Characterization of FHSS in Wireless Personal Area Networks. 39–44.
  • [13] Kriz P, Maly F, Kozel T. 2016. Improving Indoor Localization Using Bluetooth Low Energy Beacons. Mobile Information Systems, 1–11.
  • [14] Dag, T., Arsan, T., 2017. Received signal strength based least squares lateration algorithm for indoor localization. Computers & Electrical Engineering (in Press).
  • [15] Xu, J., Ma, M., Law, C. 2008. AOA Cooperative Position Localization. In: Proceedings of the global telecommunications conference, IEEE GLOBECOM 2008, 1–5 .
  • [16] Lee, Y. 2011. Weighted-average based aoa parameter estimations for LR-UWB wireless positioning system. IEICE Transactions on Communications, 94, 3, 599–602 .
  • [17] Dardari, D., Conti, A., Ferner, U., Giorgetti, A., Win M.Z. 2009. Ranging with ultrawide bandwidth signals in multipath environments. IEEE Proceedings, 97, 404–26.
  • [18] Alsindi, N., Alavi, B., Pahlavan, K. 2007. Spatial characteristics of UWB TOAbased ranging in indoor multipath environments. In: Proceedings of the 18th IEEE international symposium on personal, indoor and mobile radio communications, Athens, Greece, 1–6.
  • [19] Alsindi, N.A., Alavi, B., Pahlavan, K. 2009. Measurement and modeling of ultra wide band TOA–based ranging in indoor multipath environments. IEEE Trans Veh Technologies, 58 (3), 1046 – 58.
  • [20] Liu, H., Darabi, H., Banerjee, P., Liu, J. 2007. Survey of wireless indoor positioning techniques and systems. IEEE Trans Syst Man Cybernet Part C, 37 (6), 1067 – 80.
  • [21] Gezici, S. 2008. A survey on wireless position estimation. Wireless Pers Communicaiton, 44 (3), 263 – 82.
  • [22] Samuel, H., Connell, S., Milligan, I., Austin, D., Hayes, T.L., Chiang, P. 2011. Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials. In: Proceedings of the 33rd annual international conference of the IEEE engineering in medicine and biology society (EMBS ’11), 7598–7601.
  • [23] Pai, D., Malpani, M., Sasi, I., Aggarwal, N., Mantripragada, P.S. 2012. A Robust pedestrian dead reckoning system on smartphones. In: Proceedings of the IEEE 11th international conference on trust, security and privacy in computing and communications (TrustCom ’12), 2000–2007.
  • [24] Bahl, P., Padmanabhan, V.N.2000. RADAR: an in-building RF-based user location and tracking system. IEEE Infocom 2000, Tel Aviv, Israel, 2, 775–84.
  • [25] Saha, S., Chaudhuri, K., Sanghi, D., Bhagwat, P. 2003. Location determination of a mobile device using IEEE 802.11b access point signals. In: IEEE wireless communications & networking conference (WCNC), vol. 3, 1987–92.
  • [26] Moghtadaiee, V., Dempster, A.G. 2015. Determining the best vector distance measure for use in location fingerprinting. Pervasive Mobile Computing, 23, 59–79.
  • [27] Erol, O.K., Eksin, I. 2006. A new optimization method: Big Bang – Big Crunch. Advances in Engineering Software, 37(2), 106–11.
  • [28] Chen, Z., Zou, H., Jiang, H., Zhu, Q., Soh, Y., Xie, L., 2015. Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization. Sensors 15, 715–732.
  • [29] Arsan, T., Kepez, O., 2017. Early Steps in Automated Behavior Mapping via Indoor Sensors. Sensors 17, 2925.
  • [30] Li, X., Wang, J. and Liu, C., 2015. “A Bluetooth/PDR Integration Algorithm for an Indoor Positioning System,” Sensors, vol. 15, no. 10, pp. 24862–24885
  • [31] Tuncer, T., 2017. “Intelligent Centroid Localization Based on Fuzzy Logic and Genetic Algorithm”, International Journal of Computational Intelligence Systems, Vol. 10 (2017) 1056–1065.
There are 31 citations in total.

Details

Journal Section Articles
Authors

Taner Arsan

Publication Date October 5, 2018
Published in Issue Year 2018 Volume: 22 Issue: Special

Cite

APA Arsan, T. (2018). Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22, 367-374.
AMA Arsan T. Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi. J. Nat. Appl. Sci. October 2018;22:367-374.
Chicago Arsan, Taner. “Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 22, October (October 2018): 367-74.
EndNote Arsan T (October 1, 2018) Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 22 367–374.
IEEE T. Arsan, “Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi”, J. Nat. Appl. Sci., vol. 22, pp. 367–374, 2018.
ISNAD Arsan, Taner. “Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 22 (October 2018), 367-374.
JAMA Arsan T. Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi. J. Nat. Appl. Sci. 2018;22:367–374.
MLA Arsan, Taner. “Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 22, 2018, pp. 367-74.
Vancouver Arsan T. Büyük Patlama – Büyük Çöküş Optimizasyon Yöntemi Kullanılarak Bluetooth Tabanlı İç Mekan Konum Belirleme Sisteminin Doğruluğunun İyileştirilmesi. J. Nat. Appl. Sci. 2018;22:367-74.
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