Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking
Resource discovery in delay-tolerant mobile social networks (MSN) continues to be challenging issue. An interests and behavior prediction-based dynamic resource discovery mechanism (IBRD) in 3-dimensional cartesian coor-dinate system was proposed. Firstly, IBRD extracted the interest vectors from th...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2016-04-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016070/ |
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| Summary: | Resource discovery in delay-tolerant mobile social networks (MSN) continues to be challenging issue. An interests and behavior prediction-based dynamic resource discovery mechanism (IBRD) in 3-dimensional cartesian coor-dinate system was proposed. Firstly, IBRD extracted the interest vectors from the user's file resources and the profile table, and then the initial virtual interest communities through the cosine similarity computation between the nodes were con-structed. After mobile social networking data was anal the semi-Markov chain model was used to predict the beha-vior and movement trajectory of users. According to the prediction results of the Markov model, the dynamic ma en-ances of the virtual interest communities were realized. Next, an efficient resource discovery strategy based on the dynamic virtual interest communities was designed. Finally, proposed method was simulated on the platform of the opportunistic network environment simulator. Simulations results show that the proposed scheme consistently outperforms the state-of-the-art resource discovery schemes in terms of the searching efficiency the average delay and the communication cost. |
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| ISSN: | 1000-436X |