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SSR: Using the Social Similarity to Improve the Data Forwarding Performance in Mobile Opportunistic Networks

机译:SSR:使用社交相似性来提高移动机会主义网络中的数据转发性能

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摘要

Two nodes with similar social behavior mean that they contact each other frequently. Therefore, forwarding packets to them can efficiently improve the performance of routing algorithms in mobile opportunistic networks (MONs). Previous works use different factors to evaluate the similarity between nodes. However, they neglect the importance of nodes, which has a big influence on the system performance. How to use social importance to compute node similarity is still an open issue in MONs. This paper proposes SSR, a social similarity-based routing algorithm combining the context of social importance. First, nodes record the social importance of encounters in their buffer. Second, whenever two nodes are in contact, a dynamic time warping algorithm is used to determine the similarity of the two nodes' social importance sequences. The more similar the two sequences are, the more similar the social behavior of the two nodes is. Finally, the packet is always forwarded to the relay node with the most similar social behavior to the destination node to ensure the delivery efficiency. The simulation results show that compared with the traditional social routing algorithm, SSR significantly improves the packet delivery rate and reduces the delivery delay, cost, and hops.
机译:两个具有相似社交行为的节点意味着它们经常互相联系。因此,转发数据包可以有效地提高移动机会网络(MONS)中路由算法的性能。以前的作品使用不同的因素来评估节点之间的相似性。然而,他们忽略了节点的重要性,这对系统性能产生了很大影响。如何使用社会重要性来计算节点相似性仍然是Mons的开放问题。本文提出了一种基于社会重要性的社会相似性的路由算法SSR。首先,节点记录缓冲区中遇到的社会重要性。其次,每当两个节点接触时,动态时间翘曲算法用于确定两个节点的社会重要序列的相似性。两个序列越相似,两个节点的社交行为越多。最后,将数据包始终将其与目标节点最相似的社交行为转发到中继节点以确保输送效率。仿真结果表明,与传统的社会路由算法相比,SSR显着提高了分组传递速率并降低了交付延迟,成本和跳跃。

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