Improving Artificial Neural Network Indoor Positioning System Accuracy using Hybrid Method

Improving Artificial Neural Network Indoor Positioning System Accuracy using Hybrid Method
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© 2021 by IJETT Journal
Volume-69 Issue-11
Year of Publication : 2021
Authors : Julio Tanadi, Benfano Soewito
DOI :  10.14445/22315381/IJETT-V69I11P220

How to Cite?

Julio Tanadi, Benfano Soewito, "Improving Artificial Neural Network Indoor Positioning System Accuracy using Hybrid Method," International Journal of Engineering Trends and Technology, vol. 69, no. 11, pp. 156-160, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I11P220

Abstract
The indoor positioning system is widely used nowadays, increasing the favors in indoor positioning systems significantly. The usefulness of the indoor positioning system in many aspects, such as security, item tracking, and many others, is why an indoor positioning system is widely used. Unlike the positioning system that uses satellite signals, indoor positioning systems have superiority in dealing with signal difficulties in closed environments, which is why indoor positioning systems are becoming increasingly popular. Much research looks at improving this indoor positioning system`s accuracy by using trilateration, weighted k-nearest neighbor, and artificial neural networks. The artificial neural networks were chosen compared to the weighted k-nearest neighbor and a hybrid method that combines the neighbor of the weighted k-nearest neighbor algorithm results as data for the artificial neural network. This research shows that the weighted k-nearest neighbor and artificial neural network combined as the hybrid method can significantly increase the accuracy by 25%.

Keywords
Indoor Positioning System, Weighted K-Nearest Neighbor, Artificial Neural Network, Internet of Things, Indoor Localization

Reference
[1] H. F. Zhang, Q. Y. Du, and C. F. Qiao, Present state and trends of the geoinformation industry in China, Sustain., 7( 3) (2015) 2871–2884, , doi: 10.3390/su7032871.
[2] X. Li et al., Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BeiDou, and Galileo, J. Geod., 89( 6) (2015) 607–635, doi: 10.1007/s00190-015-0802-8.
[3] Z. Chen, H. Zou, H. Jiang, Q. Zhu, Y. C. Soh, and L. Xie, Fusion of WiFi, smartphone sensors and landmarks using the kalman filter for indoor localization, Sensors (Switzerland), 15( 1) (2015) 715–732, doi: 10.3390/s150100715.
[4] T. Bagosi and Z. Baruch, Indoor localization by WiFi, Proc. - 2011 IEEE 7th Int. Conf. Intell. Comput. Commun. Process. ICCP 2011 (2011) 449–452 , doi: 10.1109/ICCP.2011.6047914.
[5] C. H. Lim, Y. Wan, B. P. Ng, and C. M. S. See, A real-time indoor WiFi localization system is utilizing smart antennas, IEEE Trans. Consum. Electron., 53(2) (2007) 618–622, doi: 10.1109/TCE.2007.381737.
[6] R. Faragher and R. Harle, Location fingerprinting with BlueTooth low energy beacons, IEEE J. Sel. Areas Commun., 33(11) (2015) 2418–2428.
[7] V. Cantón Paterna, A. Calveras Augé, J. Paradells Aspas, and M. A. Pérez Bullones, A BlueTooth low energy indoor positioning system with channel diversity, weighted trilateration and Kalman filtering, Sensors (Basel)., 17( 12) ( 2017), doi: 10.3390/s17122927.
[8] R. Faragher and R. Harle, An analysis of the accuracy of BlueTooth low energy for indoor positioning applications, 27th Int. Tech. Meet. Satell. Div. Inst. Navig. ION GNSS 2014, 1 (2014) 201–210 .
[9] Z. Ma, S. Poslad, J. Bigham, X. Zhang, and L. Men, A BLE RSSI Ranking based Indoor Positioning System for Generic Smartphones, (2017).
[10] R. F. Brena, J. P. García-Vázquez, C. E. Galván-Tejada, D. Muñoz-Rodriguez, C. Vargas-Rosales, and J. Fangmeyer, Evolution of Indoor Positioning Technologies: A Survey, J. Sensors, vol. 2017, 2017, doi: 10.1155/2017/2630413.
[11] S. He and S. H. G. Chan, Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons, IEEE Commun. Surv. Tutorials, 18( 1) (2016) 466–490,, doi: 10.1109/COMST.2015.2464084.
[12] H. M. Hussien, Y. N. Shiferaw, and N. B. Teshale, Survey on indoor positioning techniques and systems, Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, 244( 6) (2018) 46–55, doi: 10.1007/978-3-319-95153-9_5.
[13] D. C. Dimitrova, I. Alyafawi, and T. Braun, Experimental comparison of BlueTooth and wifi signal propagation for indoor localization, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7277 LNCS, no. 5533 (2012) 126–137, , doi: 10.1007/978-3-642-30630-3_11.
[14] X. Ge and Z. Qu, Optimization WIFI indoor positioning KNN algorithm location-based fingerprint, Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, 0 (2016) 135–137, doi: 10.1109/ICSESS.2016.7883033.
[15] J. Sun, J. Liu, S. Fan, and X. Lu, Application of an improved K Nearest Neighbor algorithm in WIFI indoor positioning, Lect. Notes Electr. Eng., 342 (2015) 517–524,, doi: 10.1007/978-3-662-46632-2.
[16] D. Zhou, H. Lan, G. Zhang, X. Ma, and E. Liang, WKNN indoor location clustering algorithm with triangle correction, 130 (2017) 1570–1574, doi: 10.2991/fmsmt-17.2017.305.
[17] Y. Xu, M. Zhou, W. Meng, and L. Ma, Optimal KNN positioning algorithm via theoretical accuracy criterion in WLAN indoor environment, GLOBECOM - IEEE Glob. Telecommun. Conf., (2010) 0–4, , doi: 10.1109/GLOCOM.2010.5683804.
[18] A. Gholoobi and S. Stavrou, RSS based localization using a new WKNN approach, Proc. - 7th Int. Conf. Comput. Intell. Commun. Syst. Networks, CICSyN 2015, (2015) 27–30, doi: 10.1109/CICSyN.2015.15.
[19] L. Yen, C. H. Yan, S. Renu, A. Belay, H. P. Lin, and Y. S. Ye, A modified WKNN indoor Wi-Fi localization method with differential coordinates, Proc. 2017 IEEE Int. Conf. Appl. Syst. Innov. Appl. Syst. Innov. Mod. Technol. ICASI 2017, 2 (2017) 1822–1824, doi: 10.1109/ICASI.2017.7988299.
[20] Z. Liu, X. Luo, and T. He, Indoor positioning system based on the improved W-KNN algorithm, Proc. 2017 IEEE 2nd Adv. Inf. Technol. Electron. Autom. Control Conf. IAEAC 2017, (2017) 1355–1359, doi: 10.1109/IAEAC.2017.8054235.
[21] J. Niu, B. Wang, L. Cheng, and J. J. P. C. Rodrigues, WicLoc: An indoor localization system based on WiFi fingerprints and crowdsourcing, IEEE Int. Conf. Commun., vol. 2015-Septe, (2015) 3008–3013, doi: 10.1109/ICC.2015.7248785.
[22] Y. Peng, W. Fan, X. Dong, and X. Zhang, An Iterative Weighted KNN (IW-KNN) Based Indoor Localization Method in Bluetooth Low Energy (BLE) Environment, Proc. - 13th IEEE Int. Conf. Ubiquitous Intell. Comput. 13th IEEE Int. Conf. Adv. Trust. Comput. 16th IEEE Int. Conf. Scalable Comput. Commun. IEEE Int., (2017) 794–800, doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0127.
[23] G. S. Wu and P. H. Tseng, A Deep Neural Network-Based Indoor Positioning Method using Channel State Information, 2018 Int. Conf. Comput. Netw. Commun. ICNC 2018, (2018) 290–294, doi: 10.1109/ICCNC.2018.8390298.
[24] C. Wang, Z. Shi, F. Wu, and J. Zhang, An RFID indoor positioning system using Particle Swarm Optimization-based Artificial Neural Network, ICALIP 2016 - 2016 Int. Conf. Audio, Lang. Image Process. - Proc., 2(1) (2017) 738–742, doi: 10.1109/ICALIP.2016.7846624.
[25] H. Mehmood, N. K. Tripathi, and T. Tipdecho, Indoor positioning system using Artificial Neural Network, 6(10) (2010) 1219–1225,.
[26] C. Wang, F. Wu, Z. Shi, and D. Zhang, Indoor positioning technique by combining RFID and particle swarm optimization-based back propagation neural network, Optik (Stuttg).,127( 17) (2016) 6839–6849 , doi: 10.1016/j.ijleo.2016.04.123.