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With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]