## 3 System Model Design  
Fuzzy recognition and decision-making systems [40], novel adaptive techniques with a foundation in fuzzy inference and mathematics, are emerging in recent years as a decision support system, promising to be applied to provide more accurate decisions for routing selection, and data transmission in 5G networks, and big data environments. Because the relationship between nodes' transmission characteristics and their social attributes is fuzzy and uncertain, we attempt to employ a fuzzy inference system [41] to evaluate their importance on data transmission, thereby establishing a more efficient dissemination process. Then, we develop an adaptive routing-forwarding model exploiting fuzzy recognition and decision-making for OSNs.  
The Overall Structure of Intelligent Fuzzy Decision-Making System for Opportunistic Mobile Social Networks  
Referring to the classical applied fuzzy control system [29], the overall structure of the fuzzy recognition and decision-making model for opportunistic mobile social networks can be divided into five successive phases, including information acquisition, data processing and feature extraction, pattern matching and model building, model training and testing, and decision-making. Because the operational processes in this system are independent of each other, message application carriers in the OSNs are able to gather, collate and manage a large amount of effective information from multiple different neighbor nodes synchronously. Next, the detailed description of each procedure (as shown in Figure 1) in this system is summarized as follows:  
As shown in Figure 1, message application users with mobile high-tech devices (smart phones, smart watches, tablet computers, laptops or Bluetooth headsets) in different online communities of opportunistic mobile social networks commonly present multiple disparate social characteristics, even including many significant features that facilitate the data transmission process. Consequently, several important social attributes of nodes will be collected by this system and could be adopted to evaluate the special social relationship between destination and relay nodes, which is especially conducive to establishing a sustaining and stab  
Figure 1: The overall structure of fuzzy recognition and decision-making model for opportunistic social networks.  
Then, all of the data associated with social attributes, obtained from neighbor nodes, are suitably processed from irregular and unrecognizable to regular and identifiable formats. The process of pattern matching and model building is scrupulously divided into two parts: The course of membership recognition for nodes, and the course of fuzzy relationship inference. Each membership function is determined based on the guidelines of the tripartite method. According to the concept of the intersection membership method, three different levels of a single membership degree could be generated from different membership degrees, and afterwards the final fuzzy control result is obtained from three different levels of single membership degrees on the basis of maximum membership principle.  
Afterwards, weight adjustment for the fuzzy relationship assessment matrix and data training are implemented based on the analytic hierarchy process (AHP) [30]. As shown in Figure 1, the weight matrix is gradually determined thorough the AHP after multiple systematic and manual parameter evaluations. In addition, the inference result of the fuzzy relationship will be calculated through the weight matrix and the fuzzy relationship assessment matrix. Ultimately, the processes of data fusion and data transmission decision-making are performed in this system. Message application users constantly collect and compare the transmission value of message application users, comprehensively assess the attributive characters of these neighbor nodes, and come to an accurate decision on data dissemination based on the feedback mechanism.