Development of A Wi-Fi Based Indoor Location System Using Artificial Intelligence Techniques

Yıl 2019, Cilt: 2 Sayı: 1, 8 - 12, 15.07.2019

İsmail Kırbaş , Ayhan Dükkancı

Öz

The main aim of this study is to resolve the problem of indoor positioning in closed areas, which has become a growing need nowadays, by using existing hardware solutions. Although the use of the GPS system, which requires satellite communication as an open space location solution, is very common, it cannot provide a solution for indoor. It is a well-known metric to measure signal strengths to determine distances between wireless nodes. However, the signal strength is affected by many external influences and causes erroneous measurements. With the developed approach, the transmission powers of the signals received from more than one transmitter located within a certain closed area are measured and given as an input to an artificial neural network. It has been seen that the outputs produced by the trained neural network are much more successful and reliable than the path-loss calculation.

Anahtar Kelimeler

artificial intelligence, artificial neural networks, wireless sensor networks, indoor navigation, indoor radio communication.

Kaynakça

• [1] S. Hara and D. Anzai, “Experimental Performance Comparison of RSSI- and TDOA-Based Location Estimation Methods,” in VTC Spring 2008 - IEEE Vehicular Technology Conference, 2008, pp. 2651–2655.
• [2] T. Chrysikos, G. Georgopoulos, and S. Kotsopoulos, “Site-specific validation of ITU indoor path loss model at 2.4 GHz,” in 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks Workshops, 2009, pp. 1–6.
• [3] T. Türkoral, Ö. Tamer, S. Yetiş, E. İnanç, and L. Çetin, “Short Range Indoor Distance Estimation by Using RSSI Metric,” Istanb. Univ. -J. Electr. Electron. Eng., vol. 17, no. 2, pp. 3295–3302.
• [4] B. Guan and X. Li, “An RSSI-based Wireless Sensor Network Localization Algorithm with Error Checking and Correction,” Int. J. Online Eng. IJOE, vol. 13, no. 12, p. 52, Dec. 2017.
• [5] A. Zhang, Y. Yuan, Q. Wu, S. Zhu, and J. Deng, “Wireless Localization Based on RSSI Fingerprint Feature Vector,” Int. J. Distrib. Sens. Netw., vol. 2015, pp. 1–7, 2015.
• [6] H. P. Mistry and N. H. Mistry, “RSSI Based Localization Scheme in Wireless Sensor Networks: A Survey,” in 2015 Fifth International Conference on Advanced Computing Communication Technologies, 2015, pp. 647–652.
• [7] Z. M. Livinsa and S. Jayashri, “Performance analysis of diverse environment based on RSSI localization algorithms in wsns,” in 2013 IEEE Conference on Information Communication Technologies, 2013, pp. 572–576.
• [8] K. Vadivukkarasi and R. Kumar, “A New Approach for Error Reduction in Localization for Wireless Sensor Networks,” 2012.
• [9] Y. Chen, L. Zhang, and J. Wang, “Localization indoor patient in wireless sensor networks,” in 2013 First International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech), 2013, pp. 1–3.
• [10] A. Mesmoudi, M. Feham, and N. Labraoui, “Wireless Sensor Networks Localization Algorithms: A Comprehensive Survey,” Int. J. Comput. Netw. Commun., vol. 5, no. 6, pp. 45–64, Nov. 2013.
• [11] A. T. Parameswaran, M. I. Husain, S. Upadhyaya, and others, “Is rssia reliable parameter in sensor localization algorithms: An experimental study,” in Field Failure Data Analysis Workshop (F2DA09), 2009, p. 5.
• [12] A. Kuntal, M. L. Tetarwal, and P. Karmakar, “A Review of Location Detection Techniques in Wi-Fi,” 2014.
• [13] F. İleri and M. Akar, “RSSI Based Position Estimation in ZigBee Sensor Networks,” Signal Process. Commun., p. 12.
• [14] A. Paul and T. Sato, “Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges,” J. Sens. Actuator Netw., vol. 6, no. 4, p. 24, Oct. 2017.
• [15] N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less low-cost outdoor localization for very small devices,” IEEE Pers. Commun., vol. 7, no. 5, pp. 28–34, Oct. 2000.
• [16] M. Duhan, “Study of Localisation Methods of Mobile Users in Wireless Sensor Networks,” vol. 3, no. 6, p. 6, 2014.
• [17] İ. Kırbaş and A. Kerem, “Short-Term Wind Speed Prediction Based on Artificial Neural Network Models,” Meas. Control, vol. 49, no.6, Jul. 2016.