## 5 Discussion  
Due to the randomness of node mobility and the dependence on the forwarding of relay nodes, OppNets typically have poor routing performance and unsafe data leaks. Thus, we introduce the relationship between nodes, community, and message ferry to assist forwarding decision and relay selection. (1) Complexity of the proposed algorithm \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **ECRF a** & **S-W b** & **SimBet c** & **ESR d** & **FCNS e** \\ \hline Delivery Ratio & 0.637 & 0.432 & 0.488 & 0.628 & 0.623 \\ Delay(s) & 9124 & 14,639 & 12,346 & 9860 & 9924 \\ No. of Fwd & 28.84 & 34.24 & 31.23 & 29.06 & 29.13 \\ Hop count & 3.2 & 2.9 & 2.5 & 3.1 & 3.0 \\ \hline \hline \end{tabular}  
* Enhanced community-based routing with ferry node; b Spray-and-wait algorithm; c SimBet algorithm: The messages are forwarded based on the SimBet utility; d Effective algorithm based on the relationship between nodes; Effective algorithm based on the fuzzy routing;forwarding.  
\end{table} 
Table 1: Performance of different routing in Infocom 2006. \begin{table} \begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **ECRF** & **S-W** & **SimBet** & **ESR** & **FCNS** \\ \hline Delivery Ratio & 0.877 & 0.832 & 0.848 & 0.861 & 0.857 \\ Delay(s) & 8324 & 8863 & 8634 & 8398 & 8412 \\ No. of Fwd & 9.84 & 11.84 & 10.82 & 10.13 & 10.25 \\ Hop count & 3.2 & 2.8 & 2.7 & 3.0 & 3.1 \\ \hline \hline \end{tabular}   
* **Discussion**  
Due to the randomness of node mobility and the dependence on the forwarding of relay nodes, OppNets typically have poor routing performance and unsafe data leaks. Thus, we introduce the relationship between nodes, community, and message ferry to assist forwarding decision and relay selection. (1) Complexity of the proposed algorithm  
Figure 7: Performance of community detection algorithms: **(A)** In Infocom 2006; and **(B)** in synthetic trace. As we could see, there are mainly three computing processes in our algorithm: The measurement of the relationship between nodes, the construction of the community, and the deployment of ferry nodes. They use mainly their own information of each node in each period, which do not consume much of the network resources to transmit control and maintenance information. The time complexity of three processes are, respectively, \(o(n)\), \(o(n^{2})\), and \(o(n)\). Specially, the time complexity of community detection depends on the scale parameters \(\alpha\), and is close to the linear time if \(\alpha\) is small enough. (2) Effect of parameters  
Firstly, in the calculation of the relationship metric, as in Equation (6), there is an adjustment parameter \(\alpha\) to control the proportion of historical encounters and social features in the relationship. Clearly, \(\alpha\) is an important parameter to the accurate measurement of node relationship and the construction of community. In different application cases, \(\alpha\) can take different values to obtain better network performance. In this paper, its value is 0.5, that is, the influence of historical encounters and social features is the same on the relationship between nodes. Next, the center value of nodes is a vital factor to form community, because a mistake will often lead to a very bad result. In this paper, we use a simple calculation method of the center value, as in Equation (7), in order not to waste precious and rare network resources. Finally, \(\alpha\) in Equation (9) is another important parameter, which can control the size of the community, and is related to the time complexity of community formation. The smaller the value of \(\alpha\) is, the finer the community size is, and the higher the time complexity is. (3) Limitation of proposed algorithm  
To improve the accuracy of the node relationship measurement, we used all attributes of the node, including historical encounters and social features.