Abstract
With the extensive use of mobiles, tablets, laptops and other Wi-Fi carrying handheld devices, indoor localization using Wi-Fi fingerprinting has gained much interest of researchers. Many techniques have been introduced to increase the accuracy of the localization system. Bayesian learning techniques are considered much accurate for localization but still there are some issues including zero probability and good accuracy. In this paper we introduce a unique weighting technique called improved multinomial Naive Bayes technique for localization. For data collection we used a freeware android software, Wi-Fi Analyser. Experiments are conducted in the first floor of my office using HTC One. Our technique which uses the concept of Multinomial Naïve Bayes classifier which is actually not used before in indoor localization. It provides better accuracy, resolves zero probability issue caused due to data incompleteness. It also somehow tackles with naïve Bayes issue of independencies that according to Navies Bayes all the features are independent of each other but in physical circumstances it is not the case as features are dependent sometimes so we have tried to solve this issue as well and is easy to implement as it involves less computations as compared to those weighting techniques that includes non-linear functions.
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Professor, P.K.: Enge: the global positioning system. Signals measurements and performance. Int. J. Wirel. Inf. Netw. 1(2), 83–105 (1994)
Liu, H., Darabi, H., Banarjee, P.: Survey of wireless indoor positioning and techniques. IEEE Trans. Syst. Man Cybern. Part C 37, 1067–1080 (2007)
Shchekotov, M.: Indoor localization method based on Wi-Fi trilateration technique. In: Proceeding of the 16th Conference of Fruct Association. SPIIRAS, St. Petersburg, Russia (2014)
Farid, Z., Nordin, R., Ismail, M.: Recent advances in wireless indoor localization techniques and system. J. Comput. Netw. Commun. (2013)
Fernandes, T.: Indoor localization using bluetooth. In: 6th Doctoral Symposium in Informatics Engineering (2011)
Hernandez, O., Jain, V., Chakravarty, S., Bhargava, P.: Position location monitoring. freescale.com/beyondbits (2009)
Mahfouz, M.R., Zhang, C., Merkl, B.C.: Investigation of high accuracy indoor 3D positioning using UWB technology. IEEE Trans. Microwav. Theory Tech. 56(6), 1316–1330 (2008)
Azandaryani, S.M.: Indoor localization using wifi fingerprinting. MS thesis, Atlantic University, Florida (2013)
Jin, G.Y., Lu, X.Y., Park, M.S.: Indoor localization mechanism using active RFID tags. In: IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (SUTC’06), vol. 1, pp. 5–7, June 2006
Baniukevic, A., Jensen, C.S., Lu, H.: Hybrid indoor positioning with wifi and bluetooth, architecture and performance. In: 14th IEEE International Conference on Mobile Data Management (2013)
Perala, T., Ali Lovtty, S., Piche, R.: A comparative survey of WLAN location fingerprinting methods. In: Positioning, Navigation and Communication 2009. WPNC, pp. 243–251 (2009)
Ruiz-Ruiz, A.J., Lopez-de-Teruel, P.E., Canovas, O.: Integrating probabilistic techniques for indoor localization of heterogeneous clients. In: 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–10, November 2012
Dawes, B., Chin, K.-W.: A comparison of deterministic and probabilistic methods for indoor localization. J. Syst. Softw. 84(3), 442–451 (2011)
Madigan, D., Einahrawy, E., Martin, R.P., Ju, W.H., Krishnan, P., Krishnakumar, A.S.: Bayesian indoor positioning system. In: Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 1217–1227, March 2005
Tran, K., Phung, D., Adams, B., Venkatesh, S.: Indoor location prediction using multiple wireless received signal strengths. In: 2008 Proceedings of the 7th Australasian Data Mining Conference, pp. 187–192, November 2008
Teo, C.H: Feature weighting for improved classifier Robustness. In: 6th Conference on Email and Anti-Spam, July 2009
Zhang, W., Wang, L., Qin, Z., Zheng, X., Sun, L., Jin, N.: INBS: an improved naive Bayes simple learning approach for accurate indoor localization. In: IEEE ICC 2014 - Ad-hoc and Sensor Networking Symposium (2014)
Lee, C.H.: A new fine-grained weighting method in multi-label text classification. Ph.D. thesis, University Seoul, Korea
Zhang, H., Jiang, L., Su, J.: Augmented Naïve Bayes for ranking. In: 10th IEEE International Conference on Data Mining (2012)
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Ul Haq, M.A., Kamboh, H.M.A., Akram, U., Sohail, A., Iram, H. (2018). Indoor Localization Using Improved Multinomial Naïve Bayes Technique. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_32
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DOI: https://doi.org/10.1007/978-3-319-60834-1_32
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