Abstract
Dış mekanlarda konum belirlemek için küresel konumlama sistemi, küresel uydu seyir sistemi veya cep telefonu baz istasyonları günlük hayatımızda yaygın olarak kullanılmaktadır. Fakat bina içleri gibi kapalı alanlarda bu yöntemler etkin olarak kullanılamamaktadır. Bu nedenle kapalı ortamlarda da çalışabilecek etkin konum belirleme sistemlerine ve yöntemlerine ihtiyaç vardır. Bu çalışmada, alınan sinyal gücü göstergesi (Received Signal Strength Indicator - RSSI) verisine dayalı bir konum belirleme yöntemi olan parmak izi tabanlı konum belirleme sistemlerinin hata oranlarının azaltılmasına yönelik yeni bir sistem modeli sunulmuştur. Bu yöntemde, öncelikle çok yollu yayılımın sinyal gücü üzerindeki etkisini azaltmak için parmak izi yönteminde oluşturulan ortamın radyo haritasının boyutunun küçültülmesi (ortamın hücrelere bölünmesi) amaçlanmıştır. Bunun için sınıflandırma yöntemlerinden destek vektör makinesi (Support Vector machine, SVM) kullanılmıştır. Son olarak gezgin cihazın konum tespiti için, her bir hücrede elde edilen RSSI değerlerine göre Yapay Sinir Ağı (YSA) ile çevrimdışı eğitim yapılmıştır. Çevrimdışı eğitilen ağ ve gezgin cihazdan sabit cihazlara gelen RSSI değerleri kullanılarak çevrimiçi gezgin cihazın konum tespiti yapılmıştır. Önerilen yöntemin, literatürde sıklıkla kullanılan üçgenleme ve YSA ile konum belirleme yöntemlerinden daha etkin olduğu gösterilmiştir.
Supporting Institution
Zonguldak Bülent Ecevit Üniversitesi
Project Number
2019-75737790-02
References
- [1] E. Sugawara and H. Nikaido, “Wireless Sensor Networks,” Antimicrob. Agents Chemother., vol. 58, no. 12, pp. 7250–7, Dec. 2014.
- [2] F. Zafari, A. Gkelias, and K. K. Leung, “A Survey of Indoor Localization Systems and Technologies,” IEEE Commun. Surv. Tutorials, 2019.
- [3] T. S. Vanamalini and K. Lakshmi Joshitha, “RSSI Aided Cartographic Indoor Tracking System using Wireless Sensor Network,” Int. J. Eng. Res., vol. V4, no. 03, pp. 394–398, 2015.
- [4] M. Uradzinski, H. Guo, X. Liu, and M. Yu, “Advanced Indoor Positioning Using Zigbee Wireless Technology,” Wirel. Pers. Commun., 2017.
- [5] S. A. Mitilineos, D. M. Kyriazanos, O. E. Segou, J. N. Goufas, and S. C. A. Thomopoulos, “Indoor localization with wireless sensor networks,” Prog. Electromagn. Res., vol. 109, no. June, pp. 441–474, 2010.
- [6] Y. Zhao, Y. Liu, and L. M. Ni, “VIRE: Active RFID-based localization using virtual reference elimination,” in Proceedings of the International Conference on Parallel Processing, 2007.
- [7] T. Saray, A. Cetinkaya, and S. E. Mendi, “Monitoring of miner by RF signal,” 2017.
- [8] A. PAL, “Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges,” Netw. Protoc. Algorithms, vol. 2, no. 1, pp. 45–74, 2010.
- [9] C.-Y. Chen, “A Fuzzy Indoor Positioning System with ZigBee Wireless Sensors,” J. Electr. Electron. Eng., vol. 4, no. 5, p. 97, 2016.
- [10] E. Erdem, T. Tuncer, and R. Doğan, “Localization of a mobile device with sensor using a cascade artificial neural network-based fingerprint algorithm,” Int. J. Comput. Intell. Syst., 2018.
- [11] K. R. Athira and A. Babu, “Zigbee based indoor location tracking and monitoring system,” Int. J. Recent Technol. Eng., 2019.
- [12] G. Y. Yetkin Tatar, “Kablosuz Sensör Ağlarinda Küçültülmüş Radyo Haritasi Kullanan İmza Tabanli Dinamik Konum Bulma Tekniği,” vol. 29, no. 2, pp. 217–226, 2014.
- [13] A. Loganathan, N. S. Ahmad, and P. Goh, “Self-adaptive filtering approach for improved indoor localization of a mobile node with zigbee-based RSSI and odometry,” Sensors (Switzerland), vol. 19, no. 21, 2019.
- [14] J. M. Zurada, “Artificial Neural Systems,” p. 764, 1992.
- [15] F. Kara, H. Kaya, O. Erkaymaz, and E. Ozturk, “Prediction of the optimal threshold value in DF relay selection schemes based on artificial neural networks,” in Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016, 2016.
Abstract
In outdoor localization, global positioning system, global satellite navigation system or cell phone base stations are widely used in our daily life. However, these methods cannot be used effectively in indoor areas. Thus, effective indoor localization systems and methods, can work in closed environments, are needed. In this study, a new system model is presented to decrease the error rates of localization systems on the basis of fingerprint, which based on the received signal strength indicator (RSSI) information. In this method, firstly it is aimed to decrease the size of the fingerprint radio map (dividing the area into cells), created in the fingerprint method, in order to reduce the effect of multipath propagation on the signal strength. Hence, support vector machine (SVM), one of the classification methods, has been used. Finally, to determine mobile device' location, offline training has been conducted with Artificial Neural Network (ANN) according to RSSI values obtained in each cell. By using the offline trained network and the RSSI information, which arrived from mobile device to fixed devices, mobile device' location is determined. It is shown that the proposed method is more effective than triangulation and ANN localization methods, which are frequently used in the literature.
Keywords
Project Number
2019-75737790-02
References
- [1] E. Sugawara and H. Nikaido, “Wireless Sensor Networks,” Antimicrob. Agents Chemother., vol. 58, no. 12, pp. 7250–7, Dec. 2014.
- [2] F. Zafari, A. Gkelias, and K. K. Leung, “A Survey of Indoor Localization Systems and Technologies,” IEEE Commun. Surv. Tutorials, 2019.
- [3] T. S. Vanamalini and K. Lakshmi Joshitha, “RSSI Aided Cartographic Indoor Tracking System using Wireless Sensor Network,” Int. J. Eng. Res., vol. V4, no. 03, pp. 394–398, 2015.
- [4] M. Uradzinski, H. Guo, X. Liu, and M. Yu, “Advanced Indoor Positioning Using Zigbee Wireless Technology,” Wirel. Pers. Commun., 2017.
- [5] S. A. Mitilineos, D. M. Kyriazanos, O. E. Segou, J. N. Goufas, and S. C. A. Thomopoulos, “Indoor localization with wireless sensor networks,” Prog. Electromagn. Res., vol. 109, no. June, pp. 441–474, 2010.
- [6] Y. Zhao, Y. Liu, and L. M. Ni, “VIRE: Active RFID-based localization using virtual reference elimination,” in Proceedings of the International Conference on Parallel Processing, 2007.
- [7] T. Saray, A. Cetinkaya, and S. E. Mendi, “Monitoring of miner by RF signal,” 2017.
- [8] A. PAL, “Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges,” Netw. Protoc. Algorithms, vol. 2, no. 1, pp. 45–74, 2010.
- [9] C.-Y. Chen, “A Fuzzy Indoor Positioning System with ZigBee Wireless Sensors,” J. Electr. Electron. Eng., vol. 4, no. 5, p. 97, 2016.
- [10] E. Erdem, T. Tuncer, and R. Doğan, “Localization of a mobile device with sensor using a cascade artificial neural network-based fingerprint algorithm,” Int. J. Comput. Intell. Syst., 2018.
- [11] K. R. Athira and A. Babu, “Zigbee based indoor location tracking and monitoring system,” Int. J. Recent Technol. Eng., 2019.
- [12] G. Y. Yetkin Tatar, “Kablosuz Sensör Ağlarinda Küçültülmüş Radyo Haritasi Kullanan İmza Tabanli Dinamik Konum Bulma Tekniği,” vol. 29, no. 2, pp. 217–226, 2014.
- [13] A. Loganathan, N. S. Ahmad, and P. Goh, “Self-adaptive filtering approach for improved indoor localization of a mobile node with zigbee-based RSSI and odometry,” Sensors (Switzerland), vol. 19, no. 21, 2019.
- [14] J. M. Zurada, “Artificial Neural Systems,” p. 764, 1992.
- [15] F. Kara, H. Kaya, O. Erkaymaz, and E. Ozturk, “Prediction of the optimal threshold value in DF relay selection schemes based on artificial neural networks,” in Proceedings of the 2016 International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2016, 2016.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Makaleler |
| Authors | |
| Project Number | 2019-75737790-02 |
| Publication Date | May 31, 2020 |
| Submission Date | March 19, 2020 |
| Acceptance Date | May 10, 2020 |
| Published in Issue | Year 2020 Volume: 7 Issue: 2 |
