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Routing algorithm based on triangular fuzzy layer model and multi-layer clustering for opportunistic network

机译:基于三角形模糊层模型的路由算法和机会网络的多层聚类

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摘要

With the development of 5G network and big data and the popularity of mobile intelligent devices, the opportunistic social network has been further developed. At present, several existing routing algorithms based on node similarity use the context information of the node to select the best relay node. However, most opportunistic social algorithms only consider the social properties of nodes and ignore the importance of the similarity of the moving trajectories of the nodes. The transmission opportunity of messages in the opportunity social network is generated by the movement of the nodes, so this feature must betaken into account in the designing of the routing algorithm. Therefore, this study proposes a routing algorithm based on the triangular fuzzy layer model and multi-layer clustering for the opportunistic social network. In this study, the authors use the fuzzy analytic hierarchy process model to analyse the social similarity and trajectory similarity to determine the best message transmission node. This study compares the other four opportunistic social network routing algorithms in the simulation environment. In general, among the five routing algorithms, the transmission rate of the TFMC algorithm is the best. The average end-to-end delay and average network overhead are also the lowest.
机译:随着5G网络和大数据的发展和移动智能设备的普及,已经进一步开发了机会主义的社交网络。目前,基于节点相似度的若干现有路由算法使用节点的上下文信息来选择最佳的中继节点。然而,大多数机会主义的社交算法仅考虑节点的社交属性,并忽略节点的移动轨迹的相似性的重要性。机会社交网络中的消息的传输机会由节点的移动生成,因此必须在设计路由算法的设计中考虑该功能。因此,本研究提出了一种基于三角形模糊层模型和机会社交网络的多层聚类的路由算法。在本研究中,作者使用模糊分析层次过程模型来分析社会相似性和轨迹相似性以确定最佳的消息传输节点。本研究比较了仿真环境中的其他四个机会社交网络路由算法。通常,在五个路由算法中,TFMC算法的传输速率是最好的。平均端到端延迟和平均网络开销也是最低的。

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