## 4 Experiment and analysis
### Simulation Result Analysis
#### 4.2.1 Performance of MSSN Algorithm under Different Mobile Similarity Thresholds  
This section mainly describes the performance of the MSSN data transmission strategy under different mobile similarity thresholds. The purpose of setting the mobile similarity threshold is to reduce the number of neighbor nodes that perform the next step of calculating social similarity with the destination node, thereby reducing the calculation workload of the next step to reduce the delay and improving the reliability and accuracy of the obtained relay node. Therefore, the value of mobile similarity determines the range of relay nodes selected according to the node social similarity in the second step, which has a significant influence on the transmission performance of the MSSN algorithm. According to the simulation experiment results, when the mobile similarity threshold between the neighbor node and the destination node is set to 0.54-0.55 the MSSN data forwarding strategy shows the optimal performance in the transmission environment. First, we explore the impact of the mobile similarity threshold on the delivery ratio. As show in Figure 5, when the mobile similarity threshold is from 0.15 to 0.55, the delivery ratio is in a rising state, and the highest value even reaches 0.91, but the rising range gradually decreases. When the mobile similarity threshold rises from 0.55 to 0.75, the delivery ratio drops sharply, and even drops to 0.79. When the mobile similarity threshold rises, we no longer consider those nodes with lower similarity to the destination node. At the same time, after removing those nodes with lower mobile similarity, the higher the accuracy and reliability of the relay nodes obtained by the next step of calculating the social similarity operation. However, when the mobile similarity is higher than a certain value, some neighbor nodes that may also be suitable relay nodes are removed although more reliable relay nodes can be obtained, Consequently, the number of relay nodes we obtain is small, which leads to a lower delivery ratio. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **Spray and Wait** & **Epidemic** & **ICMT** & **EIMST** \\ \hline Delivery ratio & 15\% & 60\% & 7\% & 6\% \\ Average end-to-end delay & 90\% & 75\% & 67\% & 50\% \\ Average network overhead & 68\% & 44\% & 24\% & 13\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average improvement percentage of MSNN as compared to other four algorithms. Figure 5: Delivery ratio with various mobile similarity threshold. Figure 6 shows the effect of the mobile similarity threshold on the average end-to-end delay. As shown, when the mobile similarity threshold rises from 0.15 to 0.55, the average end-to-end delay drops from 98 to 80, and its downward trend tends to be flat. That is because as the mobile similarity threshold increases, neighbor nodes that are almost impossible to become reliable relay nodes due to the small similarity of movement to the destination node are removed, thereby greatly reducing the delay of routing selection. Since some noise nodes are reduced, the message carrier can find the appropriate relay node faster and forward the message out, the average end-to-end delay is reduced. However, when the mobile similarity threshold is higher than 0.55, the average end-to-end delay increases rapidly, even reaching 95, and the upward trend is increasing. That's because when the mobile similarity threshold exceeds a certain level, the probability and number of suitable relay nodes will be reduced due to the large number of neighbor nodes being abandoned, and the probability of destination node receiving messages will be reduced, leading to high average end-to-end delay. Finally, the effect of different mobile similarity thresholds on the overhead on average is shown in Figure 7. As we can see, when the mobile similarity threshold increases gradually from 0.15 to 0.55, the average cost decreases rapidly and even drops to 183.