**5. Conclusions**

We proposed a routing decision method based on an improved probability model combined with a quantitative social relationship value and cooperative value to filter neighbor nodes. The algorithm combines multiple feature information between nodes and uses this feature information to quantify social relationship values and partnership values. Then, the prediction matrix was obtained by matrix decomposition and gradient descent, and the relay nodes were filtered according to the predicted probability values. In our model, we first quantified the node social relationship value and the cooperative relationship value based on the collected information to form the social relationship matrix and the cooperation relationship matrix. Then, we used them to update and predict the probability of encountering cooperation between nodes in the way we proposed. Finally, in the transmission phase, the node requested a probability table associated with the destination node, and selected a node with a high probability of encountering the destination node as the next hop node. The simulation results show that the protocol performs better than the SISW, CAOF, and SIaOR transmission models in the transmission success rate, average hop count, and overhead. For a single node, the model optimizes the path from the source node to the target node. For the entire network, the performance of the network is improved to accommodate large-scale data transmission in the 5G. In the future, we will use real data sets to simulate real-world scenarios and explore other more efficient ways to improve information transmission.

**Author Contributions:** G.Y., Z.C. and J.W. (Jia Wu) conceived the idea of the paper. G.Y., Z.C., J.W. (Jia Wu) and J.W. (Jian Wu) drafted the manuscript and collected the data, wrote the code and performed the analysis; Z.C. contributed reagents/materials/analysis tools; G.Y. wrote and revised the paper.

**Funding:** This research was funded by [The Major Program of National Natural Science Foundation of China] gran<sup>t</sup> number [No. 71633006]; [The National Natural Science Foundation of China] gran<sup>t</sup> number [No. 616725407] [No. 61379057]; [China Postdoctoral Science Foundation funded project] gran<sup>t</sup> number [2017M612586]; [The Postdoctoral Science Foundation of Central South University] gran<sup>t</sup> number [185684].

**Acknowledgments:** This work was supported partially by "Mobile Health" Ministry of Education—China Mobile Joint Laboratory.

**Conflicts of Interest:** The authors declare that they have no competing interests.
