# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 6 Conclusions  
This research suggests a modified Markov path prediction algorithm for nodes in campus opportunity networks with specific movement patterns. Students are typically thought to travel in small groups and repeat themselves. The proposed routing strategy is more effective according to these two forwarding methods we present in the paper for nodes: in-group forwarding and out-group forwarding. We first allow the message to reach its group as quickly as possible, then it is further forwarded based on the influence of the node within the group, as nodes with higher influence have more access to the destination node of the message. Moreover, we discovered that storage capacity of nodes is constrained and typically small. As a result, we propose a cache management strategy in this paper. When the node's own cache space is insufficient, the message utility value is calculated based on the message diffusion and the energy consumption to the current node, and the messages with high utility value will be reserved first, achieving a reasonable cache allocation.  
The communication between nodes in the same group will be closer as the suggested method is for nodes on campus, and we hope that the following work will result in greater cooperation between nodes in the same group.  
Conceptualization, Y.C. ([PERSON]) and P.L.; methodology, Y.C. ([PERSON]); software, Y.C. ([PERSON]); validation, Y.C. ([PERSON]); formal analysis, P.L.; investigation, T.L.; resources X.W. ([PERSON]); data curation, X.W. ([PERSON]); writing--original draft preparation, Y.C. ([PERSON]); writing--review and editing, Y.C. ([PERSON]); visualization, Y.C. ([PERSON]); supervision, Y.C. ([PERSON]); project administration, P.L.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Success Rate** & **Overhead** & **Latency** & **Packet** & **Total Score** \\
**/Score** & & & & & \\ \hline Epidemic & 0.6064 & 0 & 0.1506 & 0 & 0.757 \\ Prophet & 0.34914 & 0.6095 & 0 & 0.3269 & 1.28554 \\ RDR & 0.4633 & 0.9084 & 0.8289 & 1 & 3.2006 \\ FCIM & 0 & 0.6547 & 0.6942 & 0.765 & 2.1139 \\ MPCM & 1 & 1 & 1 & 0.9492 & 3.9492 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Normalized scores with different TTL.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Success Rate** & **Overhead** & **Latency** & **Packet** & **Total Score** \\
**/Score** & & & & & \\ \hline Epidemic & 0.3631 & 0 & 0 & 0 & 0.3631 \\ Prophet & 0 & 0.2107 & 0.2123 & 0.1656 & 0.5886 \\ RDR & 0.8048 & 0.776 & 1 & 1 & 3.5808 \\ FCIM & 0.2226 & 0.0354 & 0.7517 & 0.8169 & 1.8266 \\ MPCM & 1 & 1 & 0.8713 & 0.9753 & 3.8466 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Normalized scores for different message generation intervals.  
**Funding:** This work is partly supported by the National Key R and D Program of China under grant No. 2020 YFC1523305, Key Laboratory Funds of the Ministry of Culture and Tourism under grant No 2022-13, the National Natural Science Foundation of China under Grant No. 61877037, 61872228, 61977044, the Shaanxi Key Science and Technology Innovation Team Project under Grant No. 2022 TD-26.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We appreciate the anonymous reviewers' and editorial team members' suggestions and comments. Thanks for the support of the fund projects mentioned above.  
**Conflicts of Interest:** The authors declare no conflict of interest.