Modelling of Indoor Positioning Systems Based on Location Fingerprinting

Authors

  • Mrindoko R. Nicholaus Department of Information and Communication Technology, Open University of Tanzania, Dar es Salaaam, Tanzania
  • Edephonce Nfuka Department of Information and Communication Technology, Open University of Tanzania, Dar es Salaaam, Tanzania
  • Kenedy A. Aliila K.A. Aliila is with Department of Electronics and Telecommunications, Dar es Salaam Institute of Technology, Dar es Salaaam, Tanzania

Keywords:

WLAN, Fingerprinting, Indoor positioning, Probabilistic

Abstract

In recent years, localization systems for indoor vicinity using the present wireless local area (WLAN) network infrastructure have been proposed. Such positioning systems create the usage of location fingerprinting instead of direction or time of arrival techniques for deciding the location of mobile users. However experimental study associated to such localization systems have been proposed, high attenuation and signal scattering related to greater density of wall attenuation still affecting the indoor positioning performance. This paper presents an analytical model for minimizing high signal attenuation effect for WLAN fingerprinting indoor positioning systems. The model employs the probabilistic algorithm that using signal relation method.

References

Khalajmehrabadi, A., N. Gatsis, and D. Akopian, Modern WLAN fingerprinting indoor positioning methods and deployment challenges. IEEE Communications Surveys & Tutorials, 2017. 19(3): p. 1974-2002.

. Jiang, L., A WLAN fingerprinting based indoor localization technique. 2012.

. Chen, P., et al. Survey of WLAN fingerprinting positioning system. in Applied Mechanics and Materials. 2013. Trans Tech Publ.

. Liu, K., et al., An analysis of impact factors for positioning performance in WLAN fingerprinting systems using Ishikawa diagrams and a simulation platform. Mobile Information Systems, 2017. 2017.

. Tian, X., et al., Optimization of fingerprints reporting strategy for WLAN indoor localization. IEEE Transactions on Mobile Computing, 2017. 17(2): p. 390-403.

. Halshami, I., N.A. Ahmad, and S. Sahibuddin. Adapted Indoor Positioning Model Based on Dynamic WLAN Fingerprinting RadioMap. in SoMeT. 2014.

. Tian, Z., et al., Fingerprint indoor positioning algorithm based on affinity propagation clustering. EURASIP Journal on Wireless Communications and Networking, 2013. 2013(1): p. 1-8.

. Li, B. and K. O’Keefe. WLAN TOA ranging with GNSS hybrid system for indoor navigation. in of In Proceedings of the 26th International Technical Meeting of The Satellite Division of the Institute of Navigation. 2013.

. Zhou, M., et al., Achieving cost-efficient indoor fingerprint localization on WLAN platform: A hypothetical test approach. IEEE Access, 2017. 5: p. 15865-15874.

. He, S. and S.-H.G. Chan, Wi-Fi fingerprint-based indoor positioning: Recent advances and comparisons. IEEE Communications Surveys & Tutorials, 2015. 18(1): p. 466-490.

. Lohan, E.S., et al. Received signal strength models for WLAN and BLE-based indoor positioning in multi-floor buildings. in 2015 International Conference on Localization and GNSS (ICL-GNSS). 2015. IEEE.

. Nicholaus, M.R., E.N. Nfuka, and K.A. Greyson, Properties of WLAN Indoor Fingerprinting Received Signal Strength for Localization.

. Chen, L., et al., An improved algorithm to generate a Wi-Fi fingerprint database for indoor positioning. Sensors, 2013. 13(8): p. 11085-11096.

. Park, C. and S.H. Rhee. Indoor positioning using Wi-Fi fingerprint with signal clustering. in 2017 International Conference on Information and Communication Technology Convergence (ICTC). 2017. IEEE.

. Kaemarungsi, K., Design of indoor positioning systems based on location fingerprinting technique. 2005, University of Pittsburgh.

. Roos, T., et al., A probabilistic approach to WLAN user location estimation. International Journal of Wireless Information Networks, 2002. 9(3): p. 155-164.

. Youssef, M., HORUS: A WLAN-based indoor location determination system. Department of Computer Science, University of Maryland, 2004.

. Youssef, M. and A. Agrawala. The Horus WLAN location determination system. in Proceedings of the 3rd international conference on Mobile systems, applications, and services. 2005.

. Kokkinis, A., et al. Map-aided fingerprint-based indoor positioning. in 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). 2013. IEEE.

. Bisio, I., et al., A trainingless WiFi fingerprint positioning approach over mobile devices. IEEE Antennas and Wireless Propagation Letters, 2014. 13: p. 832-835.

. Wu, C., Z. Yang, and Y. Liu, Smartphones based crowdsourcing for indoor localization. IEEE Transactions on Mobile Computing, 2014. 14(2): p. 444-457.

. Feng, C., et al., Received-signal-strength-based indoor positioning using compressive sensing. IEEE Transactions on mobile computing, 2011. 11(12): p. 1983-1993.

. Zou, H., et al., A fast and precise indoor localization algorithm based on an online sequential extreme learning machine. Sensors, 2015. 15(1): p. 1804-1824.

. Alias, M.Y., C. Sapumohotti, and S.W. Tan. Access point selection for WLAN indoor localization systems using RF walk test data. in Progress In Electromagnetics Research Symposium Proceedings. 2013. PIERS.

. K. Khaoampai, K. Na Nakorn, and K. Rojviboonchai, "FloorLoc-SL: Floor localization system with fingerprint self-learning mechanism," International Journal of Distributed Sensor Networks, vol. 11, p. 523403, 2015.

. L. Zheng, "An Optimization Approach to Indoor Location Problem Based on Received Signal Strength," 2012.

. A. Puussaar, "Indoor Positioning Using WLAN Fingerprinting with Post-Processing Scheme," Tartu Ülikool, 2014.

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Published

2020-09-22

How to Cite

Nicholaus, . M. R. ., Nfuka, E. ., & Aliila, K. A. . (2020). Modelling of Indoor Positioning Systems Based on Location Fingerprinting. International Journal of Computer (IJC), 39(1), 59–78. Retrieved from https://www.ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1819

Issue

Vol. 39 No. 1 (2020)

Section

Articles

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