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Opportunistic network enables users to form an instant network for data sharing, which is a type of Ad-hoc network in nature, thus depends on cooperation between nodes to complete message transmission. Because of intermittent communication and frequent changes of topology structure in opportunistic networks, the duration of node encounters is limited, as well as the length of established connections. If the amount of interactive data is large and the communication bandwidth is small, the messages that need to be transmitted are not guaranteed to be delivered successfully every time. In this regard, this paper establishes a transmission prediction mechanism exploiting comprehensive node forwarding capability (TPMEC) in opportunistic networks. When quantifying the forwarding capability of nodes, the algorithm not only considers the cooperative tendency but also discusses the encounter strength between nodes. At the same time, in order to find out all key nodes during the transmission process, the algorithm adopts the theory of matrix decomposition to predict and supplement the missing forwarding capability value of nodes, thus improving the efficiency of message transmission. Simulation results show that compared with ITPCM algorithm, ETNS algorithm, Spray and Wait algorithm, and PRoPHET algorithm, the proposed scheme has the highest transmission success ratio and the lowest routing overhead.

With the development of network technology and the advent of 5G communication era, highfrequency radio waves with high bandwidth will become the main choice of signal sources. While promoting the development of a high-speed communication network, a high-frequency radio wave has the problem of limited coverage, which makes opportunistic social network become the mainstream communication method. Since the transmission of a large amount of data in a short time will cause the problem of data redundancy, opportunistic social networks suggest that the most appropriate next hop should be selected to achieve efficient data transmission. At present, there are several routing algorithms based on social relations, which attempt to select the most suitable next-hop node among neighbor nodes by making use of relevant context information and historical interaction between nodes. However, existing data transmission methods in opportunistic social networks mainly focus on the influence of a few social attributes on the similarity between nodes but ignore the transmission preference caused by individual characteristics of nodes. To improve the transmission efficiency, this paper establishes an effective data transmission strategy (ENPSR) exploiting node preference and social relations in opportunistic social networks. In our scheme, individual transmission preferences are obtained by measuring the social attributes and historical information of nodes in the transmission process. The appropriate message delivery decision is determined by the prediction scheme, and the continuous and stable data transmission are realized through the recommendation mechanism. According to the simulation experiments, the average delivery ratio of ENPSR algorithm is 0.85, which is 20% higher than that of the epidemic algorithm.