基于兴趣和行为预测的移动社交网络动态资源发现机制

李致远; 陈汝龙; 王汝传

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

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基于兴趣和行为预测的移动社交网络动态资源发现机制

学术论文 | 更新时间：2024-06-05

- 基于兴趣和行为预测的移动社交网络动态资源发现机制
- Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking
- 通信学报 2016年37卷第4期页码：34-43
- 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：
  
  [ "李致远（1981-），男，河南开封人，江苏大学副教授、硕士生导师，主要研究方向为移动社交网络、移动P2P网络和物联网的资源发现与资源共享。" ]
  [ "陈汝龙（1989-），男，山东潍坊人，江苏大学硕士生，主要研究方向为移动社交网络的资源发现和资源共享等。" ]
  [ "王汝传（1943-），男，安徽合肥人，南京邮电大学教授、博士生导师，主要研究方向为计算机软件、计算机网络和网格、信息安全、无线传感器网络、移动代理等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61202474);国家自然科学基金资助项目(61373017);国家自然科学基金资助项目(61572260);国家自然科学基金资助项目(61572261);中国博士后科学基金资助项目(2015M570469);江苏省自然科学基金资助项目(BK20130528);镇江市重点研发计划（社会发展）基金资助项目(SH2015020);江苏大学高级专业人才科研启动基金资助项目(12JDG049)
- DOI：10.11959/j.issn.1000-436x.2016070
  中图分类号： TP393
- 网络出版日期：2016-04，
  
  纸质出版日期：2016-04-25
- 稿件说明：
移动端阅览
李致远, 陈汝龙, 王汝传. 基于兴趣和行为预测的移动社交网络动态资源发现机制[J]. 通信学报, 2016,37(4):34-43.

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.
李致远, 陈汝龙, 王汝传. 基于兴趣和行为预测的移动社交网络动态资源发现机制[J]. 通信学报, 2016,37(4):34-43. DOI： 10.11959/j.issn.1000-436x.2016070.

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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Keywords

references

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