Abstract
In addition to being a fundamental infrastructure for communication, cellular networks are employed for positioning through signal fingerprinting. In this respect, the choice of the specific strategy used to obtain a position estimation from fingerprints plays a major role in determining the overall accuracy. In this paper, a new machine learning approach, based on decision tree ensembles, is outlined and evaluated against a set of well-known, state-of-the-art fingerprint comparison functions from the literature. Tests are carried out with different tracking devices and environmental settings. It turns out that the proposed approach provides consistently better estimations than the other considered functions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bahl P, Padmanabhan VN (2000) Radar: an in-building RF-based user location and tracking system. In: Proceedings of of the 19th INFOCOM, vol 2. IEEE, pp 775–784
Bozkurt S, Elibol G, Gunal S, Yayan U (2015) A comparative study on machine learning algorithms for indoor positioning. In: International symposium on innovations in intelligent SysTems and applications (INISTA), pp 1–8. https://doi.org/10.1109/INISTA.2015.7276725
Caffery J, Stuber GL (1998) Subscriber location in CDMA cellular networks. IEEE Trans Veh Technol 47(2):406–416
Chen MY, Sohn T, Chmelev D, Haehnel D, Hightower J, Hughes J, LaMarca A, Potter F, Smith I, Varshavsky A (2006) Practical metropolitan-scale positioning for GSM phones. In: Proceedings of the 8th UbiComp. Springer, pp 225–242
Deblauwe N (2008) GSM-based positioning: techniques and applications. ASP
Deligiannis N, Louvros S, Kotsopoulos S (2007) Mobile positioning based on existing signalling messages in GSM networks. In: Proceedings of the 3rd MOBIMEDIA
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10–18. https://doi.org/10.1145/1656274.1656278
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction, 2nd edn. Springer
Jedari E, Wu Z, Rashidzadeh R, Saif M (2015) Wi-fi based indoor location positioning employing random forest classifier. In: 2015 international conference on indoor positioning and indoor navigation (IPIN), pp 1–5. https://doi.org/10.1109/IPIN.2015.7346754
Kjrgaard MB, Munk CV (2008) Hyperbolic location fingerprinting: a calibration-free solution for handling differences in signal strength (concise contribution). In: 2008 Sixth annual IEEE international conference on pervasive computing and communications (PerCom), pp 110–116. https://doi.org/10.1109/PERCOM.2008.75
Li X, Zhang X, Chen K, Feng S (2014) Measurement and analysis of energy consumption on android smartphones. In: Proceedings of the 4th ICIST. IEEE, pp 242–245. https://doi.org/10.1109/ICIST.2014.6920375
Meniem MHA, Hamad AM, Shaaban E (2013) Relative RSS-based GSM localization technique. In: IEEE international conference on electro-information technology, EIT 2013, pp 1–6. https://doi.org/10.1109/EIT.2013.6632643
Ngai EWT, Hu Y, Wong YH, Chen Y, Sun X (2011) The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis Support Syst 50(3):559–569. https://doi.org/10.1016/j.dss.2010.08.006
Paek J, Kim KH, Singh JP, Govindan R (2011) Energy-efficient positioning for smartphones using cell-id sequence matching. In: Proceedings of the 9th MobiSys. ACM, pp 293–306
PostgreSQL Global Development Group (2008) PostgreSQL. http://www.postgresql.org
Qi Y, Kobayashi H, Suda H (2006) Analysis of wireless geolocation in a non-line-of-sight environment. IEEE Trans Wirel Commun 5(3):672–681
Retscher G, Joksch J (2016) Comparison of different vector distance measure calculation variants for indoor location fingerprinting. In: Proceedings of the 13th international conference on location-based services, ICA, pp 53–76
Romero C, Ventura S (2010) Educational data mining: a review of the state of the art. IEEE Trans Syst Man Cybern Part C 40(6):601–618. https://doi.org/10.1109/TSMCC.2010.2053532
Sánchez D, Quinteiro JM, Hernández-Morera P, Martel-Jordán E (2012) Using data mining and fingerprinting extension with device orientation information for WLAN efficient indoor location estimation. In: 2012 IEEE 8th international conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp 77–83
Sohn T, Varshavsky A, LaMarca A, Chen MY, Choudhury T, Smith I, Consolvo S, Hightower J, Griswold WG, de Lara E (2006) Mobility detection using everyday GSM traces. Springer, Berlin, Heidelberg, pp 212–224. https://doi.org/10.1007/11853565_13
Spirito MA, Mattioli AG (1998) On the hyperbolic positioning of GSM mobile stations. In: Proceedings of ISSSE ’98. IEEE, pp 173–177
Tomar D, Agarwal S (2013) A survey on data mining approaches for healthcare. Int J Bio-Sci Bio-Technol 5(5):241–266
Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
Zekavat R, Buehrer RM (2011) Handbook of position location: theory, practice and advances, 1st edn. Wiley-IEEE Press
Zhuang Z, Kim KH, Singh JP (2010) Improving energy efficiency of location sensing on smartphones. In: Proceedings of the 8th MobiSys. ACM, pp 315–330
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Viel, A., Brunello, A., Montanari, A., Pittino, F. (2018). An Original Approach to Positioning with Cellular Fingerprints Based on Decision Tree Ensembles. In: Kiefer, P., Huang, H., Van de Weghe, N., Raubal, M. (eds) Progress in Location Based Services 2018. LBS 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-71470-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-71470-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-71469-1
Online ISBN: 978-3-319-71470-7
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)