## 2 Related Work
Some researchers have proposed that the cost of cellular network transmission is high and it is not conducive to increasing user participation. Therefore, researchers focus on the application of wireless communication technology in MCSN by constructing a new opportunistic propagation model. They proposed an opportunistic data transmission mechanism based on the Socialization Susceptible Infected Susceptible Epidemic Model (SSIS) [23]. The mechanism uses the SSIS model to obtain a social relationship table by analyzing the social relationships of mobile nodes. The source node that performs the sensing task through the combination of the social relationship table and the spray and wait mechanism selectively propagates the data to other nodes until it reaches the destination node that can send the data to the platform. Using the spray and wait method will make the transmission mode more complicated and the node energy consumption increase. Some researchers have proposed a new mobile opportunistic network routing protocol, MLProph [24], through machine learning and through further research on the mobile social network. The model uses decision trees and neural networks to train various factors, such as the predicted value inherited from the PROPHET routing scheme, node popularity, node's power consumption, speed, and location, to further calculate the probability of successful delivery of information. The algorithm trains based on historical information to obtain an equation for calculating the probability to detect whether the node can send the message to the destination node. This is an interesting way to make probabilistic predictions through machine learning methods, however, this method may only consider the nodes themselves, without considering the characteristics between the nodes. According to the discussion on those methods, there is no complicated decision-making scheme considering the cooperative relationship and social relationship of the nodes. Additionally, the key neighbors are calculated and decided by nodes. When the amount of data transmission is large, calculation and decision by nodes may increase energy consumption. We need more complicated decision-making methods to solve data transmission problems more conveniently when the amount of data skyrockets in 5G networks. A good and effective decision-making mode determines whether the information transmission between mobile devices can achieve the desired effect. Many researchers have proposed a probabilistic routing method to calculate the probability of a nodes' final successful delivery to the destination by training the probabilistic equation. However, these methods could not consider whether the selected relay node is willing to cooperate in forwarding messages. Furthermore, nodes move randomly and socially, and it is worth considering whether such social relations will affect the activity rules of nodes. Therefore, we designed a model based on probabilistic prediction by quantifying social relationships and cooperative relationship values. We need to analyze and process and make decisions on a large amount of data, and improve the user satisfaction through active caching of edge devices to meet the needs of users in the 5G network [20]. In this case, we can make transmission calculations and decisions through small base stations or edge devices to reduce the workload of mobile nodes. Consequently, we have collected the characteristic information of nodes in the region through the base station and quantified the collected information to form the encounter matrix, cooperative matrix, and social relation matrix, respectively. Then, we used the improved probability matrix decomposition method to integrate the matrix information to update the encounter probability matrix and predict the probability value of the encounter and cooperation between two nodes without the encounter history information and updated the existing value. Finally, the neighbor node is filtered by the size of EC in the updated probability matrix.