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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2018 - 14

Mobile Subscriber Profiling and Personal Service Generation using Location Awareness

OZTOPRAK, K. See more information about OZTOPRAK, K. on SCOPUS See more information about OZTOPRAK, K. on IEEExplore See more information about OZTOPRAK, K. on Web of Science
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Download PDF pdficon (889 KB) | Citation | Downloads: 454 | Views: 1,268

Author keywords
social network services, artificial neural networks, data mining, real-time systems, cooperative communication

References keywords
mobile(12), profiling(11), user(9), communications(7), prediction(6), networks(6), mobility(5), machine(5), computing(5), telecommunications(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 105 - 112
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03014
Web of Science Accession Number: 000442420900014
SCOPUS ID: 85052113265

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In the mobile environment, the location and the next move of subscribers are important. In this study, a method to detect the next move of the subscribers is proposed. In addition to the categorization of subscribers by using their Internet usage history, the knowledge of the next move pattern of subscribers will provide the flexibility to guide them to decide the next move. During the tracking of subscribers, the mobile devices of the subscribers are used as sensors to get in-depth knowledge about their preferences in their social life. The method presented here is the first in the literature to estimate the next move without connecting to any social networks. It combines the geographic locations and the Internet usage of the subscribers in order to predict their movement. In addition, most of the IoT studies either concentrate on network topologies or power consumption, while in this study, dynamicity and exact location estimation are utilized to handle the challenges and attain the required results. The results of the experiments show that the proposed system predicts the next move of a subscriber with a precision of more than 90 percent.

References | Cited By  «-- Click to see who has cited this paper

[1] P. R. Ltd, "Portio Research Mobile Factbook 2013," Portio Research Mobile Factbook 2013 Report accessed online on August 1, 2015, 2015. [Online] Available: Temporary on-line reference link removed - see the PDF document

[2] R. van der Meulen, "Analysts to explore the value and impact of iot on business at gartner symposium/itxpo 2015, november 8-12 in Barcelona, Spain."

[3] B. C. Villaverde, R. de Paz Alberola, A. J. Jara, S. Fedor, S. K. Das, and D. Pesch, "Service discovery protocols for constrained machine-to- machine communications." IEEE Communications Surveys and Tutorials, vol. 16, no. 1, pp. 41-60, 2014.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 53]

[4] P. K. Verma, R. Verma, A. Prakash, A. Agrawal, K. Naik, R. Tripathi, M. Alsabaan, T. Khalifa, T. Abdelkader, and A. Abogharaf, "Machine- to-machine (m2m) communications: A survey," Journal of Network and Computer Applications, vol. 66, pp. 83-105, 2016.
[CrossRef] [Web of Science Times Cited 95] [SCOPUS Times Cited 107]

[5] K. Oztoprak, "Profiling Subscribers According to Their Internet Usage Characteristics and Behaviors," in Proceedings of the IEEE Big Data. IEEE, 2015, pp. 1492-1499, Santa Clara, October 2015.
[CrossRef] [SCOPUS Times Cited 7]

[6] K. Oztoprak, "Subscriber Profiling for Connection Service Providers by Considering Individuals and Different Timeframes," IEICE Transactions on Communications, vol. 99, no. 6, 2016.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 5]

[7] X. Chang, Y. Yi, Y.Lingyun, S. Purui,and F. Dengguo,"Automated User Profiling in Location-Based Mobile Messaging Applications," in Trust, Security and Privacy in Computing and Communications (TrustCom), 2014 IEEE 13th International Conference on. IEEE, September 2015, pp. 18-26.
[CrossRef] [Web of Science Record] [SCOPUS Times Cited 1]

[8] P. Burge and J. Shawe-Taylor, "An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection," Journal of Parallel and Distributed Computing, vol. 61, no. 7, pp. 915-925, 2001.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 41]

[9] P. Loskot, M. A. M. Hassanien, F. Farjady, M. Ruffini, and D. Payne, "Long-term drivers of broadband traffic in next-generation networks," Annals of Telecommunications - Annales Des Telecommunications, vol. 70, no. 1-2, pp. 1-10, 2014.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 10]

[10] J. Ghosh, M. J. Beal, H. Q. Ngo, and C. Qiao, "On Profiling Mobility and Predicting Locations of Wireless Users," International Workshop on Multi-hop Ad Hoc Networks: from Theory to Reality, pp. 55-62, 2006.
[CrossRef] [SCOPUS Times Cited 37]

[11] S. Lee, J. Lim, J. Park, and K. Kim, "Next place prediction based on spatiotemporal pattern mining of mobile device logs," Sensors, vol. 16, no. 2, p. 145, 2016.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 31]

[12] "Open RTB API Specifications v2.3.1, accessed online on August 1st of 2016," The Interactive Advertising Bureau (IAB) Web Site, 2015. [Online] Available: Temporary on-line reference link removed - see the PDF document

[13] L. Vintan, A. Gellert, J. Petzold, and T. Ungerer, "Person movement prediction using neural networks," Technical Report of University of Augsburg, vol. April, no. 10, 2004.

[14] A. Gellert and L. Vintan, "Person Movement Prediction Using Hidden Markov Models," Studies in Informatics and Control, vol. 15, no. 1, pp. 17-30, 2006. [Online] Available: Temporary on-line reference link removed - see the PDF document

[15] S. Akoush and A. Sameh, "Mobile user movement prediction using bayesian learning for neural networks," Proceedings of the 2007 international conference on Wireless communications and mobile computing - IWCMC '07, p. 191, 2007.
[CrossRef] [SCOPUS Times Cited 49]

[16] N. Phamand, T. Cao, Knowledge and Systems Engineering: Proceedings of the Fifth International Conference KSE 2013, Volume 1. Cham: Springer International Publishing, 2014, ch. A Spatio-Temporal Profiling Model for Person Identification, pp. 363-373.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]

[17] L. Cortesao, F. Martins, A. Rosa, and P. Carvalho, "Fraud Management Systems in Telecommunications: a practical approach," in Proceeding of 12th International Conference on Telecommunications, 2005, pp. 167- 182.

[18] M. Lin, H. Cao, V. Zheng, K. C.-c. Chang, and S. Krishnaswamy, "Mobility Profiling for User Verification with Anonymized Location Data," in Proceedings of the 24th International Conference on Artificial Intelligence. AAAI Press, 2015, pp. 960-966.

[19] C. S. Hilas and J. N. Sahalos, "User profiling for fraud detection in telecommunication networks," 5th International Conference on Technology and Automation, pp. 382-387, 2005.

[20] M. M. Ko and M. M. S. Thwin, "Anomalous Behavior Detection in Mobile Network," in Genetic and Evolutionary Computing. Springer International Publishing, 2015, pp. 147-155.

[21] J. H. Schumann, F. von Wangenheim, and N. Groene, "Targeted Online Advertising: Using Reciprocity Appeals to Increase Acceptance Among Users of Free Web Services," Journal of Marketing, vol. 78, no. 1, pp. 59-75, 2014.
[CrossRef] [Web of Science Times Cited 101] [SCOPUS Times Cited 121]

[22] H. Haddadi, P.Hui,and I.Brown, "MobiAd: private and scalable mobile advertising," Proceedings of the fifth ACM international workshop on Mobility in the evolving internet architecture, pp. 33-38, 2010.
[CrossRef] [SCOPUS Times Cited 53]

[23] I. Ullah, R. Boreli, M. A. Kaafar, and S. S. Kanhere, "Characterising user targeting for in-app mobile ads," in Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on. IEEE, 2014, pp. 547-552.
[CrossRef] [SCOPUS Times Cited 14]

[24] H. Fujimoto, M. Etoh, A. Kinno, and Y. Akinaga, "Web user profiling on proxy logs and its evaluation in personalization," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6612 LNCS, pp. 107-118, 2011.
[CrossRef] [SCOPUS Times Cited 9]

[25] G. Castellano,A.M.Fanelli, C. Mencar, and M.A.Torsello,"Similarity- Based Fuzzy Clustering for User Profiling," 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, pp. 75-78, 2007.
[CrossRef] [Web of Science Times Cited 15]

[26] K. Makvana and P. Shah, "A novel approach to personalize web search through user profiling and query reformulation," in Data Mining and Intelligent Computing (ICDMIC), 2014 International Conference on. IEEE, 2014, pp. 1-10.
[CrossRef] [SCOPUS Times Cited 20]

[27] M. D.Hall and N.Kanar, "Method delivering location-base targeted advertisements to mobile subscribers," US Patent: US7027801 B1, 2006.

[28] J. Wilson, C. Kachappilly, R. Mohan, P. Kapadia, A. Soman, and S. Chaudhury, "Real World Applications of Machine Learning Techniques over Large Mobile Subscriber Datasets," arXiv:1502.02215 [cs], pp. 1-9, 2015.

[29] Z. Zhang, L. Zhou, X. Zhao, G. Wang, Y. Su, M. Metzger, H. Zheng, and B. Y. Zhao, "On the validity of geosocial mobility traces," in Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks - HotNets-XII, 2013, pp. 1-7.
[CrossRef] [SCOPUS Times Cited 31]

[30] A. Noulas, S. Scellato, N. Lathia, and C. Mascolo, "Mining user mobility features for next place prediction in location-based services," Proceedings - IEEE International Conference on Data Mining, ICDM, no. April, pp. 1038-1043, 2012.
[CrossRef] [Web of Science Times Cited 158] [SCOPUS Times Cited 261]

[31] B. Zheng, K. Thompson, S. S. Lam, S. W. Yoon, and N. Gnanasambandam, "Customers' Behavior Prediction Using Artificial Neural Network," IIE Proceedings of the 2013 Industrial and Systems Engineering Research Conference, pp. 700-709, 2013.

[32] K. D. Bahn, "Characterizing Consumer Interest Through the Use of Canonical Correlation: Application for Small Business," in Proceedings of the 1982 Academy of Marketing Science (AMS) Annual Conference Part of the series Developments in Marketing Science, 1982, pp. 519-524.

References Weight

Web of Science® Citations for all references: 488 TCR
SCOPUS® Citations for all references: 853 TCR

Web of Science® Average Citations per reference: 15 ACR
SCOPUS® Average Citations per reference: 26 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-10-17 18:05 in 134 seconds.

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