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.

中文翻译：

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基于三角模糊层模型和多层聚类的机会网络路由算法

随着5G网络和大数据的发展以及移动智能设备的普及，机会主义社交网络得到了进一步发展。当前，几种基于节点相似性的现有路由算法使用节点的上下文信息来选择最佳中继节点。然而，大多数机会社会算法仅考虑节点的社会属性，而忽略了节点移动轨迹的相似性的重要性。机会社交网络中消息的传输机会是由节点的移动产生的，因此在路由算法的设计中必须考虑此功能。因此，本研究提出了一种基于三角模糊层模型和多层聚类的机会社会网络路由算法。在这个研究中，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种机会性社交网络路由算法。通常，在五种路由算法中，TFMC算法的传输速率最佳。平均端到端延迟和平均网络开销也是最低的。