Indoor Positioning and Navigating System Application Using Wi-Fi with Fingerprinting Method and Weighted K-Nearest Neighbor Algorithm

English

Authors

  • Arya Putra Hadi Yulianto Politeknik Negeri Malang
  • M. Nanak Zakaria State Polytechnic of Malang
  • Ahmad WIlda Yulianto State Polytechnic of Malang

DOI:

https://doi.org/10.33795/jartel.v12i3.493

Keywords:

Fingerprint, Indoor Positioning System, Navigation, Nearest Neighbor, RSSI

Abstract

The need for accurate indoor location determination, object tracking, digital maps and indoor travel routes is increasing along with the construction of buildings that have complex and spacious layouts. The current Global Positioning System navigation system is only effective for outdoor use. However, when used indoors it becomes inaccurate due to factors such as signal attenuation and multipath caused by wall obstructions in the building. This study designed an application of Indoor Positioning and Navigating System Using Wi-Fi with Fingerprinting method and Weighted K-Nearest Neighbor algorithm. In the design process, it is necessary to create a fingerprinting database by considering the number of Access points and environmental conditions. Based on the results of the study, the location results of the application show that from floors 1,2, and 3. Floor 1 has a room accuracy result of 89% and a point accuracy of 86% with an average deviation of 1.42 px or 0.9 m, floor 2 has room accuracy results. of 65% and a point accuracy of 70% with an average deviation of 2.43 px or 1.7 m, and the 3rd floor has a room accuracy of 86% and a point accuracy of 68% with an average deviation of 2.27 or 1.5 m. Based on the data above, this application is proven to be able to detect the position of someone in the room with a success percentage on the 1st floor by 90%, the 2nd floor by 55%, and the 3rd floor by 80%.

References

T. T. K, V.N, X. Q. P, and E. N. Huh, “Wi ? Fi indoor positioning and navigation?: a cloudlet ? based cloud computing approach,” Human-centric Comput. Inf. Sci., 2020, doi: 10.1186/s13673-020-00236-8.

A. A. Kalbandhe and S. C. Patil, “Indoor Positioning System using Bluetooth Low Energy,” Int. Conf. Comput. Anal. Secur. Trends, CAST 2016, pp. 451–455, 2017, doi: 10.1109/CAST.2016.7915011.

Y. Cui, Y. Zhang, Y. Huang, Z. Wang, and H. Fu, “Novel WiFi/MEMS integrated indoor navigation system based on two-stage EKF,” Micromachines, vol. 10, no. 3, 2019, doi: 10.3390/mi10030198.

Z. Turgut, G. Z. G. Aydin, and A. Sertbas, “Indoor Localization Techniques for Smart Building Environment,” Procedia Comput. Sci., vol. 83, no. Ant, pp. 1176–1181, 2016, doi: 10.1016/j.procs.2016.04.242.

A.A. Careem, W.H. Ali, and M.H. Jasim, 2020, April. Wirelessly indoor positioning system based on RSS Signal. In 2020 International Conference on Computer Science and Software Engineering (CSASE) (pp. 238-243). IEEE.

B. Li, Y. Wang, H. K. Lee, A. Dempster, and C. Rizos, “Method for yielding a database of location fingerprints in WLAN,” IEE Proc. Commun., vol. 152, no. 5, pp. 580–586, 2005, doi: 10.1049/ip-com:20050078.

J. Golenbiewski, and G. Tewolde, 2020, September. Wi-Fi based indoor positioning and navigation system (IPS/INS). In 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-7). IEEE.

O. Costilla-Reyes and K. Namuduri, 2014, October. Dynamic Wi-Fi fingerprinting indoor positioning system. In 2014 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 271-280). IEEE.

M. U. Ali, S. Hur, and Y. Park, “Wi-Fi-based effortless indoor positioning system using IoT sensors,” Sensors (Switzerland), vol. 19, no. 7, 2019, doi: 10.3390/s19071496.

K. Ajayannan, J. A. R, and S. Jenila, “Smart Indoor Navigation Using Wifi Triangulation,” vol. 3, no. 04, pp. 1–5, 2015.

D. P. Yudha, B. I. Hasbi, and R. H. Sukarna, “Indoor Positioning System Berdasarkan Fingerprinting Received Signal Strength (Rss) Wifi Dengan Algoritma K-Nearest NEIGHBOUR (K-Nn),” Ilk. J. Ilm., vol. 10, no. 3, pp. 274–283, 2018, doi: 10.33096/ilkom.v10i3.364.274-283.

B. Shin, J.H. Lee, T. Lee, and H.S. Kim, 2012, April. Enhanced weighted K-nearest neighbor algorithm for indoor Wi-Fi positioning systems. In 2012 8th international conference on computing technology and information management (NCM and ICNIT) (Vol. 2, pp. 574-577). IEEE.

W.K. Zegeye, S.B. Amsalu, Y. Astatke, and F. Moazzami, 2016, October. WiFi RSS fingerprinting indoor localization for mobile devices. In 2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) (pp. 1-6). IEEE.

Gupta, Suyash, Wi-Fi- based Indoor Positioning System Using Smartphones," Talentica Software, no 1, pp. 3-25,2018. Available: https://www.talentica.com/wp-content/uploads/2018/03/wifi-indoor positioning-using-smartphones.pdf

Y. A. Maulana., Implementasi Indoor Positioning System (IPS) Menggunakan Algoritma Weighted k-Nearest Neighbor di Gedung A Fakultas Teknik Universitas Jember (Doctoral dissertation, FAKULTAS TEKNIK).

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Published

2022-09-30

How to Cite

[1]
A. P. Hadi Yulianto, M. N. Zakaria, and A. W. Yulianto, “Indoor Positioning and Navigating System Application Using Wi-Fi with Fingerprinting Method and Weighted K-Nearest Neighbor Algorithm: English”, Journal of Telecommunication Networks, vol. 12, no. 3, pp. 185-191, Sep. 2022.