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Mobile Subscriber Profiling and Personal Service Generation using Location AwarenessOZTOPRAK, K.
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social network services, artificial neural networks, data mining, real-time systems, cooperative communication
mobile(12), profiling(11), user(9), communications(7), prediction(6), networks(6), mobility(5), machine(5), computing(5), telecommunications(4)
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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
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.
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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