## 4 Experiment and analysis  
### Simulation Parameters and Simulation Process  
The experiment adopts Opportunistic Network Environment (ONE) simulation platform to evaluate the performance of MSSM data transmission strategy in real environment. In addition, we compare the MSSM routing algorithm with the following four algorithms: Spray and Wait [35], Epidemic [36], EIMST (Effective Information Transmission Based on Socialization Nodes) [37], and ICMT (Information Cache Management and Data Transmission Algorithm) [20]. \"Spray and Wait\" and Epidemic are traditional and typical algorithm, while the other two algorithms are the latest routing algorithms for opportunistic social networks. In this simulation, the movement of all nodes within the communication area follows the MGMM movement model, the communication area is set to \(4500\:\mathrm{m}\times 3400\:\mathrm{m}\), the total experiment time is 2-6 h. The initial energy for each node is 100 J, each node carries 20 data packets, and sending a packet consumes 0.25 J of energy. The speed value range of each node is 1-20 m/s, which may be the moving speed of pedestrians and vehicles. Besides, the number of mobile nodes in the original data set (Infocom5, Infocom6, Cambridge, and Intel) that this simulation obtained is shown in Table 1. Then, the number of mobile nodes in the four real datasets will be set to 100, 200, 300, 400, 500, 600, and 700 (shown as Table 2), because it is a vital factor that needs to be considered carefully in this experiment. Moreover, the cache space of each node is set to 30 MB and we define six different social attributes for each node, including interest, comment, occupation, preference, and workplace. Each social attribute contains several different eigenvalues, and each node is randomly assigned the eigenvalues on the corresponding social attribute. Considering the algorithm's requirement for attribute weights in this paper, the real dataset Infocom5, Infocom6, Cambridge, and Intel are most suitable for getting the influence of each social attribute of the node on the message transmission process. The datasets can be download from CRAWDAD, and the detailed information is shown in Table 1. After analyzing the social attributes of the datasets by using the information gain method in the MATLAB platform, the weight distribution of each social attributes of the node we selected is as shown in Figure 4. We find that the social attributes of \"interest\", \"occupation\" play a more important role in the data transmission process. In addition, in the experiment, we set the mobility similarity threshold between the neighbor node and the destination node to 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75 to test the performance of the algorithm under different thresholds. In order to improve the readability of the above experimental environment settings, we constructed the Table 2 to expound the environment of the simulation experiment. Figure 4: Weight value distribution of node social attributes. The MSSM data transmission strategy is compared with the other four algorithms mentioned above to verify its performance. The experiment mainly focuses on the following parameters:  
Delivery ratio: This metric represents the probability of the neighbor and target node getting the data packet during data transmission process. It can be computed by \(D_{node}=D_{recive}/D_{send}\), where \(D_{receive}\) represents the number of messages that the destination node gets from the relay nodes, and \(D_{send}\) is the total number of messages forwarded by the rely nodes. Overhead on average: This parameter represents the overhead of a successful message forward between a pair of nodes. It can be calculated by the Equation (23), where \(T_{total\_time}\) is the total time of data transmission, \(T_{succact\_time}\) represents the total time it takes for the data to be successfully transmitted to the destination nodes.