###### Abstract  
At the dawn of big data and 5G networks, end-to-end communication with large amounts of data between mobile devices is difficult to be implemented through the traditional face-to-face transmission mechanism in social networks. Consequently, opportunistic social networks proposed that message applications should choose proper relay nodes to perform effective data transmission processes. At present, several routing algorithms, based on node similarity, attempt to use the contextual information related to nodes and the special relationships between them to select a suitable relay node among neighbors. However, when evaluating the similarity degree between a pair of nodes, most existing algorithms in opportunistic social networks pay attention to only a few similar factors, and even ignore the importance of mobile similarity in the data transmission process. To improve the transmission environment, this study establishes a fuzzy routing-forwarding algorithm (FCNS) exploiting comprehensive node similarity (the mobile and social similarities) in opportunistic social networks. In our proposed scheme, the transmission preference of the node is determined through the fuzzy evaluation of mobile and social similarities. The suitable message delivery decision is made by collecting and comparing the transmission preference of nodes, and the sustainable and stable data transmission process is performed through the feedback mechanism. Through simulations and the comparison of social network algorithms, the delivery ratio in the proposed algorithm is 0.85 on average, and the routing delay and network overhead of this algorithm are always the lowest. opportunistic social networks; routing algorithm; node similarity; fuzzy inference system; data transmission [300] [30  
At the dawn of 5G networks and big data, technological development is leading to the wide distribution of mobile devices, such as smartphones, Bluetooth, Wi-Fi technologies, or notebooks. Thus, it can be said that opportunistic social networks [10; 13] have originated from the social scene in which mobile devices carried by humans have adopted the meeting opportunities to implement peer-to-peer communication between users. A simple example of opportunistic social networks could be how mobile phone users move as they wish and communicate with each other via WiFi, Hotspot, or Bluetooth within each other's communication area. While users are out of reach of each other's transmission domain, the store-carry-forward strategy [4] is commonly applied in the data transmission process of opportunistic social networks, and nodes carrying data packets adopt the encounter opportunities created by movements to forward messages in order to relay nodes or destinations. Without an instantaneous end-to-end path, message or data transmission between source nodes and destinations may go through a long distance and cannot always be in a connected state. Furthermore, traditional face-to-face transmission methods in opportunistic social networks are difficult to implement complete big data communication without continuous connectivity. Theoretical analysis [4] points out that relay nodes can distribute parts of cache spaces for messages and properly wait for the destination. Therefore, it is particularly important for message applications to choose suitable relay nodes to perform an efficient data transmission process. Additionally, how reliable relay nodes are selected to form the surrounding neighbors is a momentous issue for routing algorithm design in opportunistic social networks. The social attribute of humans always reflects some special connections between users, which can be a strong basis for choosing between relay nodes. Research [5] has presented a comprehensive survey of human mobility of opportunistic social networks in three main aspects: the attribute characteristics of users, mobility patterns and traces, and mobility prediction methods.