Abstract
Emergency call localization is an important step in the emergency call taking process. As more than 80 % of emergency calls are dialled from mobile phones, the localization of calls in mobile networks deserves highest attention. In this paper we describe a model-based localization method which do not impose any special investments or modifications on the mobile network infrastructure. We create a model of the mobile network environment using the geographically referenced Self Organizing Map with interval values, trained on common measurements regularly performed by mobile network operator. Localization is then performed by finding the node of the model best matching to the characteristic vector of the call.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bock HH (2003) Clustering algorithms and Kohonen maps for symbolic data. New trends in computational statistics with biomedical applications. J Jpn Soc Comp Statist 15.2:217–229
D’Urso P, DeGiovanni L (2011) Midpoint radius self-organizing maps for interval-valued data with telecommunications application. Appl Soft Comput 11:3877–3886
De Carvalho F, Bertrand P, De Melo F (2012) Batch self-organizing maps based on city-block distances for interval variables, p 15 <hal-00706519>
Gan G, Ma C, Wu J (2007) Data clustering: theory, algorithms and applications. SIAM, Philadelphia
Hajjar C, Hamdan H (2011) Self-organizing map based on Hausdorff distance for interval-valued data. In: IEEE international conference on systems, man, and cybernetics (SMC), Anchorage, pp 1747–1752
Hajjar C, Hamdan H (2011) Self-organizing map based on L2 distance for interval-valued data. In: 6th IEEE international symposium on applied computational intelligence and informatics, Timişoara, 19–21 May 2011
Hajjar C, Hamdan H (2013) Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances. Neural Netw 46:124–132
Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle River
Henriques R, Bacao F, Lobo V (2012) Exploratory geospatial data analysis using the GeoSOM suite. Comput Environ Urban Syst 36:218–232
Kapicak L, Sebesta R, Michalek L, Dvorsky M (2011) Comparison method for mobile location techniques. In: 13th international conference on research in telecommunication technologies 2011, Techov, Czech Republic
Klement P, Snášel V (2009) Anomaly detection in emergency call data—the first step to intelligent emergency call system management. In: Proceedings of the 1st international conference on intelligent networking and collaborative systems (INCoS 2009), Barcelona, Spain, Nov 2009
Kohonen T (1995) Self-organizing maps. Springer, Berlin
Liu L, Xiao J, Yu L (2008) Interval self-organizing map for nonlinear system identification and control. Advances in neural networks—ISNN 2008. In: 5th international symposium on neural networks, ISNN 2008, Beijing, China, 24–28 September 2008, Proceedings, Part I, pp 78-86
Martinovič J, Novosád T, Snášel V, Scherer P, Klement P, Šebesta R (2012) Clustering the mobile phone positions based on suffix tree and self-organizing maps. Neural Netw World 22:371–386
Moore RE, Kearfott RB, Cloud MJ (2009) Introduction to interval analysis Society for industrial and applied mathematics, Philadelphia
Novosád T, Martinovič J, Scherer P, Snášel V, Klement P, Šebesta R (2011) Mobile phone positioning in GSM networks based on information retrieval methods and data structures. in: Proceedings of the international conference on digital information processing and communications (ICDIPC), published in Communications in computer and information science series of springer LNCS
Sebesta R, Dvorsky M, Kapicak L, Michalek L, Martinovič J, Scherer P (2011) Visualisation of best servers areas in GSM networks. In: 11th international conference on knowledge in telecommunication technologies and optics KTTO 2011, Szczyrk, Polan
Yang MY (2001) Extending the Kohonen self-organizing map networks for clustering analysis. Comput Stat Data Anal 38:161–180
Acknowledgments
This work was supported by the IT4Innovations Centre of Excellence project (CZ.1.05/1.1.00/02.0070), funded by the European Regional Development Fund and the national budget of the Czech Republic via the Research and Development for Innovations Operational Programme and by Project SP2015/146 “Parallel processing of Big data 2” of the Student Grand System, VŠB—Technical University of Ostrava.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Klement, P., Snášel, V. (2015). SOM on Interval Variables for Mobile Emergency Call Positioning. In: Abraham, A., Jiang, X., Snášel, V., Pan, JS. (eds) Intelligent Data Analysis and Applications. Advances in Intelligent Systems and Computing, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-21206-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-21206-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21205-0
Online ISBN: 978-3-319-21206-7
eBook Packages: EngineeringEngineering (R0)