###### Abstract
The research shows that nodes with a high social similarity are divided into the same community, and the frequent encounter between them indicates that more communication opportunities will exist so as to transmit messages to each other. Consequently, in opportunistic social networks, context-aware routing algorithms based on node similarity attempt to adopt the contextual information related to nodes, as well as the social relationships among nodes, to choose a proper relay node from the surrounding neighbors. In general, each node in an opportunistic social network may possess a great number of social attributes, and different social attributes represent different relationships between users. Therefore, different routing algorithms based on node similarity consider different similarity factors of nodes in the network, such as interests, locations, workplaces, professions, physical characteristics, or social relations. It is clear that each similarity factor is more likely to play a different role in successful data transmission. This means that transmission mechanisms based on node similarity are supposed to take into consideration a large number of similarity factors, rather than a single one. Moreover, the temporal and spatial attributes of nodes [15] represent the geographical positions of users at different times (the mobile trajectories) in opportunistic social networks. In social scenarios, humans are more likely to repeat the same mobile route every day, such as from places of residence to workplaces, meaning there is a high probability of meeting the same person each day [16]. In other words, two users may encounter each other frequently during an activity cycle if there is a relatively high degree of mobile similarity between them, which indicates message carriers can decide when and where to forward messages to the suitable relay nodes. Consequently, it is able to obtain an enhanced transmission performance to exploit the mobile similarity between users in opportunistic social networks. However, when evaluating the similarity degree between a pair of nodes, few algorithms [10; 12] synthetically consider multiple similarity factors, and mobile similarity is even disregarded. While transmission mechanisms accurately assess the similarity degree between nodes, the biggest difficulty is how to comprehensively evaluate the different effects of each similarity factor and highlight the importance of moving user trajectories in data transmission. On one hand, humans taking along mobile devices usually move as they like within a certain range [5], and the distance between them is constantly changing. Therefore, only when users are in each other's transmission range, messages can be successfully forwarded to relay nodes. Meanwhile, due to the changing location information, mobile trajectories of users in the networks are difficult measure. On the other hand, the social attributes of users (except for mobility information) in opportunistic social networks are relatively stable [15], meaning the collection and updating of effective information also becomes a significant basis for defining node similarity. For different application scenarios, each social attribute plays a different role in the transmission environment--thus, it is hard for source nodes or message carriers to comprehensively weigh the influence of a larger number of similarity factors between users. To solve these urgent issues, this work proposes a fuzzy routing-forwarding algorithm (FCNS) exploiting comprehensive node similarity (the social and mobile similarities) in opportunistic social networks. This strategy employs the social attributes and historical mobile information of nodes to determine the similarity degrees between them. Through reasonable weight adjustments and the fuzzy inference system [17], the transmission preference values are adopted to comprehensively evaluate the social and mobile similarities between nodes and the destination.