# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 5 Results
### Experimental Results Analysis
#### 5.2.3 Different Time to Live of Messages (TTL)  
By altering the time to live (TTL) of various messages at a cache size of 50 M and a message generation interval of 100s, the impact on the metrics is shown in Figure 5. The delivery success rate of each method has a trend of increasing and then decreasing as the message TTL increases. The message initially has adequate time to reach the destination node as the TTL increases. However, when the TTL rises further, a huge number of message copies will exist in the network, lowering the success rate. MPCM shows better performance when the TTL is greater than 3 h. The success rate of message delivery gradually declines as the TTL increases because the Epidemic algorithm causes a significant increase in copies and no more room to receive new messages. Each algorithm's latency will rise as the TTL rises, the MPCM will not rise any further until it reaches a more stable value, and the overall latency is very low. The network overhead rises as a result of the inability to clear message copies in time as the TTL gets longer. The TTL has the biggest impact on Epidemic's overhead, while MPCM's overhead is consistently kept at a minimal level. When the TTL is greater than 3 h, the number of packet losses in MPCM is close to 0, and most of the messages can be delivered to the destination node before the expiration date.  
Figure 4: Comparison of success rate, latency, overhead and packet loss with different message generation intervals.  
The MPCM algorithm presented in this paper performs reasonably well when compared to numerous other algorithms in various cache spaces, message generation intervals and varied message TTLs. Under all circumstances, the delivery success rate of MPCM exhibits the best performance. It also performs exceptionally well in terms of latency, overhead and packet drop which is mostly unaffected by the amount of cache capacity. When messages are excessively dense, the success rate decreases, but it still performs better than many other algorithms in general. Overall, MPCM can adapt to the campus context with a limited node cache, dense message generation and short message TTL. Experiments show that the proposed method in the paper achieves better results in terms of delivery rate, average delivery latency, overhead and several packets drop. The average values of each measure are processed using data normalization, and the experimental results under the influence of three parameters, different cache spaces, different message generation intervals and different message survival times, are tabulated in Tables 4-6 below.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \multirow{2}{*}{**Success Rate**} & \multirow{2}{*}{**Overhead**} & \multirow{2}{*}{**Latency**} & \multirow{2}{*}{**Packet**} & \multirow{2}{*}{**Total Score**} \\ /**Score** & & & & & \\ \hline Epidemic & 0 & 0.2687 & 0 & 0 & 0.2687 \\ Prophet & 0.0034 & 0.4460 & 0.0092 & 0.2374 & 0.696 \\ RDR & 0.6362 & 0.9897 & 0.9639 & 1 & 3.5898 \\ FCIM & 0.1840 & 0 & 1 & 0.7987 & 1.9827 \\ MPCM & 1 & 1 & 0.9388 & 0.9935 & 3.9323 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Normalized scores under different caches.  
Figure 5: Comparison of success rate, latency, overhead, and packet loss with different message TTL.  
The suggested algorithm in this paper has the highest score under the influence of the three parameters, yielding the best outcome.  
Our proposed approach works well in the campus context because student nodes are more regular in their movement and the message-forwarding process is not blind, which effectively limits the message copies in the network and maximizes cache space utilization. Moreover, messages are forwarded within groups based on centrality, which means that messages can be transmitted to their destinations through those relays that are more influential. However, because of their potential similarity in sparse networks, it may be impossible to identify which nodes have high centrality.