## 5 Conclusions  
This paper proposes an effective data transmission strategy algorithm (MSSN) that utilizes mobile and social similarity in opportunistic social networks. The model algorithm is mainly divided into four phases. First, in order to achieve the functional integrity of the algorithm, it is necessary to collect and update related information of the network node. Secondly, on the one hand, the mobile similarity of the nodes is calculated by the relationship between the number of co-neighbor nodes. On the other hand, through the similarity calculation of the historical movement trajectory of the node, the final one node mobile similarity is obtained by combining the weights. Then, the partial nodes with high mobile similarity are selected by the single threshold screening method to proceed to the next step. Thirdly, the retained nodes are selected by the clustering algorithm, and the distance between the Minkowski distance and the MVD distance is calculated, and the distance of the social attribute is calculated according to the social attribute. The larger the distance value is, the smaller the similarity is. Finally, the relay node with the highest probability of message forwarding success is selected and the entire forwarding strategy is described.  
This work adopts the social and mobile relationships between a pair of nodes, termed as \"node similarity\", to improve data dissemination environment in opportunistic social networks. However, frequent information exchange among mobile devices may lead to the disclosure of private attribute and data protection. Alternatively, the memory and energy of mobile devices are also big limitations on data dissemination process in opportunistic social networks. Therefore, in future work, we apply this algorithm in different real-world scenarios, continuously improve the performance of the algorithm, and consider the issues of energy consumption and information security. To be specific, we will build an information protection protocol framework, which can be applied in the network layer, to hide the vital information about mobile nodes in the bundle layer of opportunistic networks and protect  
Figure 10: Overhead on average with different number of nodes.  
private data of mobile devices. Alternatively, we will integrate more node attributes to achieve a better data transmission environment, which is reflected in higher delivery ratio, lower average end-to-end delay and average network overhead.  
**Author Contributions:** [PERSON] and [PERSON] conceived the idea of the paper. [PERSON] designed and performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote and revised the paper.  
**Funding:** This research was funded by [Scientific Research Fund of Hunan Provincial Education Department, Research on database storage performance optimization in virtualization] grant number [No. 16C1498]; [School level scientific research project of XiangNan University, Research on network security situation prediction based on data fusion] grant number [No. 2017X]16.  
**Conflicts of Interest:** The authors declare that they have no competing interests.