Abstract
Accurate mapping and localization of an environment have been improved owing to the advancements in mobile internet technology. However, indoor localization still requires more intelligent algorithms to keep continuous track of a mobile user because of presence of obstacles and satellite’s incapability. The paper presents an indoor wifi based algorithm that combines fingerprint and least square algorithms to track location of a mobile user. The fingerprint data is computed in terms of received signal strength (RSS) acquired from different access points at predefined locations, dynamically. Similarly, the mobile user coordinate is estimated by involving digital filtering process followed by least square technique. The variance in RSS is observed between fingerprint and least square algorithm and applied to Kalman filter for the estimation of weightage value. Thus, the combined mechanism helps to result the value with more accuracy leaving behind low accurate value. The efficiency of the proposed method is evaluated by involving both MATLAB simulation environment covers up to 30 m × 35 m and also hardware resources in real time environment covers up to 13 m × 21 m. The results have showed the accuracy of less than a meter.
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References
Liu, Y., Yang, Z.: Location, Localization, and Localizability: Location Awareness Technology for Wireless Networks. Springer, New York (2010)
Xiao, J., Zhou, Z., Yi, Y.: A survey on wireless indoor localization from the device perspective. ACM Comput. Surv. 49(2), Article no. 25 (2016)
Au, A.W.S., Feng, C., Valaee, S., Reyes, S., Sorour, S., Markowitz, S.N., Gold, D., Gordon, K., Eizenman, M.: Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device. IEEE Trans. Mob. Comput. 12(10), 2050–2062 (2013)
Torres-Sospedra, J., Moreira, A.: Analysis of sources of large localization errors in deterministic fingerprinting. Sensors 17, 2736 (2017)
Mok, E., Retscher, G.: Location determination using WiFi fingerprinting versus WiFi trilateration. 145–159 (2007)
Emery, M., Denko, M.K.: IEEE 802.11 WLAN based real-time location tracking in indoor and outdoor environments, 0840-7789 (2007)
Sakpere, W., Adeyeye Oshin, M., Mlitwa, N.B.W.: A survey on a state-of-the-art survey of indoor positioning and navigation systems and technologies. S. Afr. Comput. J. SACJ 29(3), 145–197 (2017)
Liu, K., Motta, G., Ma, T.: XYZ indoor navigation through augmented reality: a research in progress. In: IEEE International Conference on Services Computing (SCC), San Francisco, CA, pp. 299–306 (2016)
Liu, K., Motta, G., Ma, T., Guo, T.: Multi-floor indoor navigation with geo-magnetic field positioning and ant colony optimization algorithm. In: 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), Oxford, pp. 314–323 (2016)
Mendoza-Silva, G.M., Torres-Sospedra, J., Huerta, J., Montoliu, R., Benítez, F., Belmonte, O.: Situation goodness method for weighted centroid-based Wi-Fi APs localization. Springer (2017). https://doi.org/10.1007/978-3-319-47289-8_2
Wen-jian, W., Jin, L., He-lin, L., Bing, K.: An improved weighted trilateration localization algorithm. J. Zhengzhou Univ. Light. Ind. (Nat. Sci.) 3(6), 84–85 (2012)
Adler, S., Schmitt, S., Kyas, M.: Path loss and multipath effects in a real world indoor localization scenario. In: 2014 11th Workshop on Positioning, Navigation and Communication (WPNC), pp. 1–7, March 2014
Mahiddin, N.: Indoor position detection using WiFi and trilateration technique. In: The International Conference on Informatics and Applications (2012)
Yim, J., Jeong, S., Gwon, K., Joo, J.: Improvement of Kalman filters for WLAN based Indoor Tracking. Expert Syst. Appl. 37, 426–433 (2010). https://doi.org/10.1016/j.eswa.2009.05.047
Xiao, T.-T., Liao, X.-Y., Hu, K., Yu, M.: Study of fingerprint location algorithm based on WiFi technology for indoor localization. In: International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2014), 26–28 September 2014
İlçi, V., Gülal, E., Çizmeci, H., Coşar, M.: RSS-based indoor positioning with weighted iterative nonlinear least square algorithm. In: The Twelfth International Conference on Wireless and Mobile Communication, ICWMC 2016 (2016)
Subedi, S., Pyun, J.-Y.: Practical fingerprinting localization for indoor positioning system by using beacons. J. Sens. 2017, 16 (2017). Article ID 9742170
Chai, S., An, R., Du, Z.: An indoor positioning algorithm using Bluetooth low energy RSSIS. In: International Conference on Advanced Material Science and Environmental Engineering (2016)
Honkavirta, V., Perala, T., Ali-Loytty, S., Piche, R.: A comparative survey of WLAN location fingerprinting methods. In: Proceedings of the 6th Workshop on Positioning, Navigation and Communication 2009, WPNC 2009, pp. 243–251, March 2009
Acknowledgments
The authors gratefully acknowledge the financial support from Department of Science and Technology by sanctioning a project (File No: DST/SSTP/TN/29/2017-18) to Velammal Engineering College, Chennai, under SSTP scheme.
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Rajasundari, T., Balaji Ganesh, A., Hari Prakash, A., Ramji, V., Lakshmi Sangeetha, A. (2020). Improved WiFi Based Real-Time Indoor Localization Strategy. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_10
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DOI: https://doi.org/10.1007/978-3-030-34080-3_10
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