## 4 Performance Analysis
### Simulation Analysis  
The simulation has been performed by QUALNET. Simulation parameters are shown in Table 2.  
In the simulation, we set P\({}_{\text{H}}\) = 0.7, Pc = 0.1 for 90% of the nodes at each hot spot and \(P_{H}^{{}^{\prime}}\) = 0.5, \(P_{C}^{{}^{\prime}}\) = 0.1 for other 20% nodes. We compare the performance of our proposed solution with that of the Social Delay Tolerant Network based on Clustering (CS-DTN), Prophet [5] and Clustering [8]. The main difference between the CS-DTN and the CCS-DTN is that network coding has been used by the CCS-DTN scheme. Compared with the Prophet scheme, the CCS-DTN has adopted the clustering technology based on the community characteristics of the nodes in the social DTNs. The CCS-DTN has taken the advantages of the characteristics of the active nodes in the social DTNs while the Clustering schemes have not considered it. The successful delivery rate, defined as the ratio of the number of messages that have been successfully received by the destina \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation Time & 10,000 s \\ Size of Area & \(3000\times 3000\) \\ Warm-up Time & 500 s \\ Number of Nodes & 50, 20–110 \\ Traffic & CBR \\ Number of Messages for & 20 \\ Coding & \\ Transmission Range & 250 m \\ Message Size & 150 Byte \\ Number of Message Copies & 2 \\ Threshold of Clustering & 0.4 \\ Rate of Message Sending & 0.1 \\ MAC Protocol & 802.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. Figure 5: Impact of Queue Size on Delivery Ratio. messages that the source nodes have sent, and average end-to-end delay, defined as the average time used for a message transmitted from a source to its destination, have been measured. What's more, the simulation results have been compared with those obtained from the theory analysis. With the change of the buffer size at each node, the performance in terms of successful delivery ratio has been shown in Figure 5. Clearly, the successful delivery rate has been improved with the increase of the buffer size because the number of messages dropped due to overflows has been reduced. Meanwhile, as shown in Figure 6, as more messages have been kept in the buffer, the queuing delay has been increased. As a result, the average end-to-end delay has been increased. When the buffer size reaches to 140 messages, the delivery rate and average end-to-end delay become stable. It implies that the size of buffer is enough for the current states of the networks. The Clustering scheme and the proposed CCS-DTN scheme have higher delivery rate than the Prophet scheme because they fully used the community characteristics of the nodes in social DTNs. Compared with the Clustering scheme, the CCS-DTN scheme has taken full use of the active characteristics of the nodes in the social DTNs and has introduced the network coding scheme. As a result, messages will be quickly delivered to the destination and some of dropped messages could be restored at the destination by the network coding scheme. Therefore, the CCS-DTN scheme can obtain a higher delivery rate than that of the Clustering scheme when the buffer is sufficient. On the contrary, when the buffer is small, the delivery rate of the CCS-DTN scheme is lower than that of the Clustering scheme. Since messages will be gradually transmitted to the nodes with a higher centrality by the CCS-DTN scheme, the nodes with a higher centrality will have to drop messages because there is no enough buffer. Besides, when the buffer is small, the delivery rate of the CCS-DTN scheme is also lower than the result of theoretical analysis. The reason is that in theoretical analysis, messages will be concentrated to the nodes with a higher centrality which leads to a buffer overflow at these nodes. But in actual simulation, messages may be delivered before they have been forwarded to the nodes with a higher centrality. In this way, there seem more messages dropped in theoretical analysis.