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A Statistical Analysis and Temporary Cluster based Routing Algorithm for Delay Tolerant Networks

机译:基于统计分析和基于临时聚类的时延容忍网络路由算法

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Delay Tolerant Networks are characterized by frequent network topology partition, limited resource, extremely high latency, etc. Consequently successful deliveries of messages in such networks face great challenges. In this paper, by capturing the temporary clusters, we timely use the temporary end-to-end paths to directly and successfully deliver messages. Besides, by collecting a large number of encounter history information as the samples, we use the methods of statistical analysis to objectively and accurately evaluate encountered nodes, thus selecting fewer but better relay nodes to spread messages. Finally based on the above schemes, a Statistical Analysis and Temporary Cluster based routing algorithm (SATC) is proposed to improve the routing performance. Extensive simulations have been conducted and the results show that SATC can achieve a higher delivery ratio and a fewer hop count compared to Epidemic and PRoPHET. Furthermore, its overhead ratio is 70% and 65% less than Epidemic and PRoPHET respectively.
机译:时延容忍网络的特点是频繁的网络拓扑划分,有限的资源,极高的延迟等。因此,在此类网络中成功传递消息面临着巨大的挑战。在本文中,通过捕获临时群集,我们及时使用了临时的端到端路径来直接成功地传递消息。此外,通过收集大量遭遇历史信息作为样本,我们使用统计分析的方法来客观,准确地评估遇到的节点,从而选择更少但更好的中继节点来传播消息。最后,基于上述方案,提出了一种基于统计分析和基于临时聚类的路由算法(SATC),以提高路由性能。进行了广泛的仿真,结果表明,与Epidemic和PRoPHET相比,SATC可以实现更高的传递比率和更少的跳数。此外,其开销率分别比Epidemic和PRoPHET分别低70%和65%。

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