Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking

Zhi-yuan LI; Ru-long CHEN; Ru-chuan WANG

doi:10.11959/j.issn.1000-436x.2016070

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Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking

academic paper | 更新时间：2024-06-05

- Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking
- Journal of Communications Vol. 37, Issue 4, Pages: 34-43(2016)
- 作者机构：
  
  1. 江苏大学计算机科学与通信工程学院，江苏镇江212013
  2. 南京邮电大学江苏省无线传感网高技术研究重点实验室，江苏南京210003
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61202474);The National Natural Science Foundation of China(61373017);The National Natural Science Foundation of China(61572260);The National Natural Science Foundation of China(61572261);The Project Funded by China Postdoctoral Science Foundation(2015M570469);The Natural Science Foundation of Jiangsu Province(BK20130528);The Key Research and Development Program Foundation (Social Development) of Zhejiang(SH2015020);The Senior Professional Scientific Research Foundation of Jiangsu Un versity(12JDG049)
- DOI：10.11959/j.issn.1000-436x.2016070
  CLC： TP393
- Online First：2016-04，
  
  Published：25 April 2016
- 稿件说明：
移动端阅览
Zhi-yuan LI, Ru-long CHEN, Ru-chuan WANG. Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking[J]. Journal of Communications, 2016, 37(4): 34-43.
DOI：

Zhi-yuan LI, Ru-long CHEN, Ru-chuan WANG. Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking[J]. Journal of Communications, 2016, 37(4): 34-43. DOI： 10.11959/j.issn.1000-436x.2016070.

摘要

针对时延容忍的移动社交网络中的资源发现问题，在三维环境下提出一种基于兴趣和行为预测的动态资源发现机制(IBRD)。IBRD 首先从用户的文件资源和信息表中提取兴趣向量，然后通过节点间的余弦相似度计算构造初始的虚拟兴趣社区。之后，通过对移动社交数据的分析，建立半马尔可夫链模型以预测节点的行为和运动轨迹。依据模型的预测结果，实现虚拟兴趣社区的动态维护。最后，基于动态的虚拟兴趣社区构建高效的资源发现策略。IBRD机制在随机网络仿真器(ONE)上得以实现，实验结果表明，IBRD与同类模型相比，具有较高的资源发现成功率、较低的平均时延和通信开销。

Abstract

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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references

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