## 1 Introduction
In a realistic social scenario, nodes are likely to repeat the same movement route every day, such as from the residence to the workplace. If the mobile similarity of two nodes is higher, the probability of encountering between them is greater, so the probability of message delivery between them is greater. Therefore, the mobile similarity and social similarity of nodes can be used as strong basis for selecting reliable relay nodes. However, most algorithms only choose one or the other, without considering these two key factors. To tackle the above-mentioned problems, this work proposes an effective data transmission strategy (MSSN) utilizing mobile and social similarities in opportunistic social networks. This scheme evaluates the mobile similarity and social similarity between nodes by using their moving trajectories, the number of common neighbor nodes and their social attributes. Specifically, this mechanism calculates the first moving trajectory similarity between neighbor nodes and destination based on the moving trajectories of them. According to the number of their common neighbor nodes in a period of time, this method is able to determine moving trajectory similarity. The final mobile similarity can be obtained by adding together the two trajectory similarities after given the corresponding weights. Then we set a threshold based on the all obtained mobile similarity and round off the nodes whose mobile similarity is less than the threshold. Next, according to the social attributes of the remaining neighbor nodes and destination nodes, the social similarity between them is further calculated, and the nodes with higher social similarity can be the reliable relay nodes. In conclusion, the MSSSN is a novel routing-forwarding strategy, which considers the mobile similarity and social similarity of nodes together. The contributions of this paper are listed as follows:  
* By effectively collecting node information, the message applications can accurately assess the comprehensive similarity (social and mobile similarity) between neighbor nodes and destination nodes and determine the special transmission relationship between them. * Through setting a mobile similarity threshold, we can narrow down the screening range of reliable relay nodes, thereby make the next calculation of node social similarity more accurate and obtain more suitable relay nodes. * To take the social attributes of neighbor nodes and destination nodes as the main analysis basis, an effective data transmission strategy based on node socialization is proposed. Message carriers can obtain more reliable relay nodes. * In light of the simulation results in the Opportunistic Networking Environment (ONE), the performance of the MSSM algorithm is better than other algorithms in the aspects of the delivery ratio, network overhead, and end-to-end data transmission delay. The remainder of the paper is organized as follows: In Section 2, we introduce the previous related work; The MSSM data transmission strategy will be discussed in Section 3; in Section 4, we evaluate the performance of our algorithms through extensive simulation results. Finally, the conclusion of the paper is shown in the last section.