A Novel Method to Improve the Speed and the Accuracy of Location Prediction Algorithm of Mobile Users for Cellular Networks

  • Giang Minh Duc Tạp chí CNTT&TT
  • Le Manh
  • Do Hong Tuan
Keywords: Location prediction, cellular networks, Mobility prediction, Data mining, Quality of Service.

Abstract

Currently, mobile networks and their applications have been developed quickly. Mobile users not only request various types of information, but also demand on Quality of Service (QoS). One of the measures to improve QoS is to apply mobile users’ location prediction method. Mobile users’ location prediction applications include automatic bandwidth adjustment, smart handover etc. To further improve QoS, we propose a new algorithm named UMP_Add_New algorithm which helps us avoid scanning of full database again. This algorithm mines the new dataset (new transactions are added to the database)In addition, to improve the accuracy of mobile users’ location prediction, we propose a data classification method by time. The experimental results show that the UMP_Add_New algorithm has implementation time less than the UMPMining traditional algorithms did. Accuracy of the prediction by our method was also improved significantly.

Author Biography

Giang Minh Duc, Tạp chí CNTT&TT
thông tin di động, MIMO

References

Wikipedia, Gartner, "List of Countries by Number of Mobile Phones in Use," 2013.

ETSI/GSM, "Technical reports list," http://webapp.etsi.org/key/key.asp? full list=y [online].

ETSI/GSM, "Home location register/visitor location register – report 11," 2010.

Alex Cabanes, Home Location Register (HLR), I. S. &. T. Group, Ed. IBM Blade Center, June 2007.

"HRL Look Up – Service Manual. http://www.routomessaging.com".

Cristian Aflori and Mitica Craus. “Grid implementation of Apriori algorithm. Advances in engineering software”. Volume 38, Issue 5, 295-300, 2007.

Gokhan Yavas, dimitrios Katsaros. Ozgur Ulssoy and Yannis manolopoulos. “A data mining approach for location prediction in mobile environments”. Data and Knowledge Engineering, 54, 121-146, 2005.

Mohammad Waseem, R.R.Shelke, Location Pattern Mining of Users in Mobile Environment, International Journal of Electronics, Communication & Soft Computing Science and Engineering, 2013, ISSN: 2277-9477, Volume 2, Issue 9

V. Chandra Shekhar Rao and P. Sammulal. Article: Survey on sequential pattern mining algorithms. International Journal of Computer Applications, 76(12):24–31, August 2013. Published by Foundation of Computer Science, New York, USA.

A. Bhattacharya, S. K. Das, "Update: an information-theoretic approach to track mobile users in PCS networks," ACM Wireless Networks 8 (2-3), pp. 121-135, 2002.

S. rajagopal et al., "GPS-based predictive resource allocation in cellural networks," in Proceedings of the IEEE International Conference on Networks (IEEE ICON020), 2002, pp. 229-234.

Gokhan Yavas et al, "A data mining approach for location prediction in mobile environments," Data and Knowledge Engineering, vol. 54, pp. 121-146, 2005.

Cristian Aflori and Mitica Craus, "Grid implementation of Apriori algorithm," Advances in engineering software, vol. 38, pp. 295-300, 2007.

Byungjin Jeong, Seungjae Shin, Ingook Jang, Nak Woon Sung, and Hyunsoo Yoon, “A Smart Handover Decision Algorithm Using Location Prediction for Hierarchical Macro/Femto-Cell Networks “in Vehicular Conference (VTC Fall), 2011 IEEE 74th, SanFrancisco, CA, Sept 2011, pp. 1-5

Giang Minh Duc, Le Manh, Do Hong Tuan, "A Novel Location Prediction Algorithm of Mobile Users For Cellular Networks," Journal on Information Communications Technology (Research and Development on Information Communications Technology), vol. No. 8(12), pp. 58-66, Aug. 2015.

Shiby Thomas et al., "An Efficient Algorithm for the Incremental Updation of Association Rules in Large Databases," in From: KDD-97 Proceedings, 1997

G. Ramalingam, Thomas Reps, "An Incremental Algorithm for a Generalization of the Shortest-Path Problem," Technical Report # 1087, 1992.

John D. Kelleher et al., "Incremental generation of spatial referring expressions in situated dialog," in Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, Sydney, Jul. 2006, p. 1041–1048.

http://msdn.microsoft.com/en-us/ library/ bb895173.aspx (Training and Testing Data Sets – MSDN – Microsoft).

Vincent Etter et al, “Where to go from here? Mobility prediction from instantaneous information”, School of Computer and Communication Sciences, EPFL, CH-1015 Lausanne, Switzerland, 2013.

Ying Zhu Yong Sun Yu Wang, Nokia Mobile Data Challenge: Predicting Semantic Place and Next Place via Mobile Data, Mobile Data Challenge 2012 (by Nokia)

https://www.cl.cam.ac.uk/~cm542/papers/icdm2012.pdf

Published
2017-02-25
Section
Bài báo

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