Message forwarding based on periodically evolving social characteristics in opportunistic mobile networks

被引：2
作者：
School of Computer Science and Engineering, Southeast University, Nanjing [1 ]
211189, China
不详 [2 ]
211189, China
机构：
[1] School of Computer Science and Engineering, Southeast University, Nanjing
[2] Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing
来源：
Tongxin Xuebao | / 3卷
基金：
中国国家自然科学基金;
关键词：
Centrality; Community; Message forwarding; Opportunistic mobile networks; Periodic evolution;
D O I：
10.11959/j.issn.1000-436x.2015055
中图分类号：
学科分类号：
摘要：
To avoid monster community problem which suffered by distributed k-clique community detection, τ-window community detection was proposed. In addition, τ-window centrality estimation was put forward. By investigating the periodic evolution of τ-window community and τ-window centrality, two new metrics, TTL (time to live) community and TTL centrality, were proposed to improve the prediction of the node's encounter during the message's lifetime. Moreover, a social-aware routing algorithm, PerEvo, was then designed based on them. Extensive trace-driven simulation results show that PerEvo achieves higher message delivery ratio than the existing social-based forwarding schemes, while keeping similar routing overhead. ©, 2015, Tongxin Xuebao/Journal on Communications. All right reserved.
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页数：12
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