基于社会特征周期演化的机会移动网络路由转发策略

摘要

To avoid monster community problem which suffered by distributedk-clique community detection,τ-window community detection was proposed. In addition,τ-window centrality estimation was put forward. By investigating the periodic evolution ofτ-window community andτ-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.%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。