基于社会特征周期演化的机会移动网络路由转发策略  

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

作　　者：黄永锋[1] 董永强[1,2] 张三峰[1,2] 吴国新[1,2] 

机构地区：[1]东南大学计算机科学与工程学院,江苏南京211189 [2]东南大学计算机网络和信息集成教育部重点实验室,江苏南京211189

出　　处：《通信学报》2015年第3期151-162,共12页Journal on Communications

基　　金：国家自然科学基金资助项目(61272532,61370209,61300200,61472080);国家高技术研究发展计划(“863”计划)基金资助项目(2013AA013503);江苏省博士后科研基金资助项目(1402039B);江苏省未来网络前瞻性研究基金资助项目(BY2013095-2-06)~~

摘　　要：针对分布式k团社区检测引起的超大社区问题,提出了具有节点退出机制的τ-window社区检测方法,相应提出了τ-、window中心性估计。通过实验发现τ-window社区和τ-window中心性具有周期演化特性,利用该特性,提出TTL(time to live)社区检测和TTL中心性估计,以更准确预测消息生存期上节点的相遇。随后,利用TTL社区和TTL中心性作为转发测度,设计了新的机会移动网络路由算法PerEvo。实验结果表明,与现有的基于社会特征的路由算法比较,PerEvo在保持基本不变的传输开销的同时,有效提高了机会移动网络消息投递的成功率。To avoid monster community problem which suffered by distributed k-clique community detection, τ-window community detection was proposed. In addition, T-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 keep- ing similar routing overhead.

关 键 词：机会移动网络 社区 中心性 周期演化 消息转发 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]