With the rapid popularization of 5G communication and internet of things technologies, the amount of information has increased significantly in opportunistic social networks, and the types of messages have become more and more complex. More and more mobile devices join the network as nodes, making the network scale increase sharply, and the tremendous amount of datatransmission brings a more significant burden to the network. Traditional opportunistic social network routing algorithms lack effective message copy management and relay node selection methods, which will cause problems such as high network delay and insufficient cache space. Thus, we propose an opportunistic social network routing algorithm based on user-adaptive data transmission. The algorithm will combine the similarity factor, communication factor, and transmission factor of the nodes in the opportunistic social network and use information entropy theory to adaptively assign the weights of decision feature attributes in response to network changes. Also, edge nodes are effectively used, and the nodes are divided into multiple communities to reconstruct the community structure. The simulation results show that the algorithm demonstrates good performance in improving the information transmission’s success rate, reducing network delay, and caching overhead.

中文翻译：

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机会社交网络中基于用户自适应数据传输方案的路由算法

随着5G通信和物联网技术的迅速普及，机会主义社交网络中的信息量显着增加，消息类型变得越来越复杂。越来越多的移动设备作为节点加入网络，使得网络规模急剧增加，大量的数据传输给网络带来了更大的负担。传统的机会主义社交网络路由算法缺乏有效的消息复制管理和中继节点选择方法，这将导致诸如网络延迟高和缓存空间不足等问题。因此，我们提出了一种基于用户自适应数据传输的机会性社交网络路由算法。该算法将结合相似度因子，通信因子，机会社会网络中节点的传输因子和传输因子，并使用信息熵理论响应网络的变化来自适应地分配决策特征属性的权重。而且，有效地使用边缘节点，并且将节点划分为多个社区以重建社区结构。仿真结果表明，该算法在提高信息传输成功率，减少网络时延和缓存开销方面表现出良好的性能。