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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: 337 | Views: 1,014

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

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References Weight

Web of Science® Citations for all references: 449 TCR
SCOPUS® Citations for all references: 803 TCR

Web of Science® Average Citations per reference: 14 ACR
SCOPUS® Average Citations per reference: 24 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 2021-03-03 23:35 in 119 seconds.

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