Mobile social networking(MSN) has emerged as an effective platform for social network users to pervasively disseminate the contents such as news, tips, book information, music, video and so on. In content dissemination, mobile social network users receive content or information from their friends, acquaintances or neighbors, and selectively forward the content or information to others. The content generators and receivers have different motivation and requirements to disseminate the contents according to the properties of the contents, which makes it a challenging and meaningful problem to effectively disseminate the content to the appropriate users.
In this dissertation, the typical content dissemination scenarios in MSNs are investigated. According to the content properties, the corresponding user requirements are analyzed. First, a Bayesian framework is formulated to model the factors that influence users behavior on streaming video dissemination. An effective dissemination path detection algorithm is derived to detect the reliable and efficient video transmission paths. Second, the authorized content is investigated. We analyze the characteristics of the authorized content, and model the dissemination problem as a new graph problem, namely, Maximum Weighted Connected subgraph with node Quota (MWCQ), and propose two effective algorithms to solve it. Third, the authorized content dissemination problem in Opportunistic Social Networks(OSNs) is studied, based on the prediction of social connection pattern. We then analyze the influence of social connections on the content acquirement, and propose a novel approach, User Set Selection(USS) algorithm, to help social users to achieve fast and accurate content acquirement through social connections.
| Identifer | oai:union.ndltd.org:GEORGIA/oai:scholarworks.gsu.edu:cs_diss-1114 |
| Date | 14 December 2016 |
| Creators | Kong, Chenguang |
| Publisher | ScholarWorks @ Georgia State University |
| Source Sets | Georgia State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Computer Science Dissertations |
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