【摘要】 根据机会网络中拓扑的高度动态性和随时间演化的特性,提出一种基于边独立演化的时间演化图(E-TEG)模型。该模型采用马尔可夫链和生灭过程刻画演化过程的时间相关性,利用Laplace后继法则估计边的出生和死亡概率,E-TEG最终收敛于非均匀随机图。采用CRAWDAD数据集对模型进行实验,结果表明,E-TEG能够准确反映机会网络中消息传输路径的演化特性。更多还原

【Abstract】 This paper introduces an Edge-independent Time Evolving Graph(E-TEG) model to capture the evolution of the connectivity properties of Opportunistic Networks(OppNet).E-TEG model is presented through using discrete time Markovian model to deal with the time dependencies of consecutive time-step indexed network snapshots,and the dynamic of each possible edge is assumed to be an independent birth-death process.In addition,given the sequence data,the birth and the death probability of each edge are estimated through using Laplace’s rule of succession.It shows that an E-TEG eventually converges to an un-uniform random graph.E-TEG model is validated through CRAWDAD trace datasets by computing the fastest path of each pair of nodes in an instance of E-TEG.更多还原