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Seed Selection for Data Offloading Based on Social and Interest Graphs

Ying Li1, Jianbo Li1,*, Jianwei Chen1, Minchao Lu1, Caoyuan Li2,3

College of Computer Science and Technology, Qingdao University, Qingdao, 266071, China.
School of Computer, Beijing Institute of Technology, Beijing, 100081, China.
Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2006, Australia.

* Corresponding Author: Jianbo Li. Email: email.

Computers, Materials & Continua 2018, 57(3), 571-587. https://doi.org/10.32604/cmc.2018.02851

Abstract

The explosive growth of mobile data demand is becoming an increasing burden on current cellular network. To address this issue, we propose a solution of opportunistic data offloading for alleviating overloaded cellular traffic. The principle behind it is to select a few important users as seeds for data sharing. The three critical steps are detailed as follows. We first explore individual interests of users by the construction of user profiles, on which an interest graph is built by Gaussian graphical modeling. We then apply the extreme value theory to threshold the encounter duration of user pairs. So, a contact graph is generated to indicate the social relationships of users. Moreover, a contact-interest graph is developed on the basis of the social ties and individual interests of users. Corresponding on different graphs, three strategies are finally proposed for seed selection in an aim to maximize overloaded cellular data. We evaluate the performance of our algorithms by the trace data of real-word mobility. It demonstrates the effectiveness of the strategy of taking social relationships and individual interests into account.

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Cite This Article

Y. Li, J. Li, J. Chen, M. Lu and C. Li, "Seed selection for data offloading based on social and interest graphs," Computers, Materials & Continua, vol. 57, no.3, pp. 571–587, 2018.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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