基于兴趣和行为预测的移动社交网络动态资源发现机制

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

Abstract

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.

Key words: mobile social networking, dynamic resource discovery, cosine similarity, behavior prediction, Markov chain

Zhi-yuan LI,Ru-long CHEN,Ru-chuan WANG. Exploiting interests and behavior prediction for dynamic resource discovery in mobile social networking[J]. Journal on Communications, 2016, 37(4): 34-43.

Figures/Tables 10

参数	描述	默认值
MovementModel.world	仿真实验场景面积	600 m × 800 m
Size
Scenario.endTime	仿真时间	12 h
Scenario.nrofHosts	用户节点个数	100
Scenario.nrofHostGroups	分组数	5
BtInterface.transmit	传输带宽	250 kbit/s
Speed
BtInterface.transmit	传输距离	10~20 m
Range
Group.movementModel	组内节点移动模型	ShortestPathMap
		BasedMovement
Mobility Speed	节点的行走速度	0.5~1.5m/s
Message. Interval	产生消息的间隔	1~2s
Message.TTL	消息的生存周期	300 min
r	提取节点兴趣时的实验参数	0.5
$T_{s i m}^{1}$	构建局部兴趣社区的阈值	0.3
$T_{s i m}^{2}$	局部兴趣社区内资源发现的阈值	0.5
a	兴趣相似度的权重	0.6
b	运动模式相似度的权重	0.4

References 15

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