Mobile Subscriber Profiling and Personal Service Generation using Location Awareness
Abstract
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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