## 3 System Model Design
### Collecting Information about Nodes in Network
#### 3.1.2 Information Updating between Mobile Nodes  
This method contains a process of information update during the information collection phase. During the time period of the movement of the opportunistic network node, when the two nodes meet, we let the two nodes exchange information with each other. During a period of time, there will be constant nodes meeting and exchanging information between them. Finally, a mass of information network will be generated to provide support for the subsequent transmission strategy algorithms.  
\[*\textit{list\_num}=(\textit{list\_node}_{1},\textit{list\_node}_{2},\dots.\textit{list\_node}_{n}) \tag{2}\]  
Each node will meet many other nodes in a time period \(\Delta t\). Each time they meet another node, they exchange information between them. Finally, this node will contain information about many other nodes. Next, we will carry out a detailed explanation of the whole process from acquisition to update by information exchange between node \(u\) and node \(v\). As in Equation (3) below, when node \(u\) encounters node \(v\), exchange information generation matrix \(M(n)\) between two nodes.  
\[\textit{list}(u)<<=>>\textit{list}(v)=(m\_track_{node_{u}},m\_track_{node_{v }},att_{node_{u}},att_{node_{v}},\textit{*list\_num}) \tag{3}\]  
\[M(n)=(\textit{list}_{1},\textit{list}_{2}\dots\textit{list}_{n}) \tag{4}\]  
As shown in Figure 1, the process of information collection and update is performed by data exchange among mobile nodes in opportunistic social networks. To be specific, in the time T\({}_{1}\), nodes u and v are in the same communication area, and they share their own list of information sequence with each other. On the one hand, the node \(u\) establishes the status sequence \(\textit{list}_{1}\) by combining its own list of information sequence and the list of information sequence obtained from the node \(v\); on the other hand, the node \(v\) constructs the status sequence \(\textit{list}_{2}\) in the same way. Combining the two status sequences \(\textit{list}_{1}\) and \(\textit{list}_{2}\), the set matrix \(M(n)\) could be determined in the time T\({}_{1}\). With each node moves around in the communication area, at the time T\({}_{2}\), the nodes u and p are in the same community area. Then, node \(u\) shares the set matrix \(M(n)\) with the node p and the node p sends itsown list of information sequence to the node \(u\). After that, the new set matrix \(M(n)\), which consists of \(list_{1}\), \(list_{2}\) and \(list_{2}\), can be determined in the time \(\mathbb{T}_{2}\). Based on that, each node in opportunistic social networks constantly collects information from its encountered nodes and updates the set matrix \(M(n)\), which aims at establishing a uniform and regular data set in the cache spaces of each node. After that, each mobile node in opportunistic social networks accesses to the next phase, and starts to make message delivery decisions via the social and mobile information related to the network routing state.  
Through the inter-community message transmission strategy, the opportunistic social networks select the nodes with large social relationships in each community as the intermediate nodes; and sends the message to the community where the destination node is located to realize the message transmission process across the community. The node must have the movement of the node during the transmission process, and the node movement information also serves as a kind of social attribute. Selecting the mobile similarity of nodes in a relay node with high probability is an important basis for selection. The mobile and social similarities of mobile nodes in opportunistic social networks will be computed by the latest set matrix \(M(n)\), and message carriers or source nodes select suitable relay nodes from their neighbors based on the mobile and social similarities of mobile nodes in the same communication area.