Affiliations: [a] School of Computer Science and Engineering, Changsha University, Changsha, China
| [b] State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| [c] School of Computer Science and Engineering, Central South University, Changsha, China
| [d] Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton VIC, Australia
Correspondence:
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Corresponding authors: Fangfang Gou, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China. E-mails: [email protected] (F.G.) and [email protected] (J.W.)
Abstract: With the development of Internet of Things technology, 5G communication has gradually entered people’s daily lives. The number of network users has also increased dramatically, and it has become the norm for the same user to enjoy the services provided by multiple network service providers and to complete the exchange and sharing of a large amount of information at the same time. However, the existing opportunistic social network routing is not sufficiently scalable in the face of large-scale network data. Moreover, only the transaction information of network users is used as the evaluation evidence, ignoring other information, which may lead to the wrong trust assessment of nodes. Based on this, this study proposes an algorithm called Trust and Evaluation Mechanism for Users Based on Opportunistic Social Network Community Classification Computation (TEMCC). Firstly, communication communities are established based on community classification computation to solve the problem of the explosive growth of network data. Then a trust mechanism based on the Bayesian model is established to identify and judge the trustworthiness of the recommended information between nodes. This approach ensures that more reliable nodes can be selected for interaction and complete data exchange. Through simulation experiments, the delivery rate of this scheme can reach 0.8, and the average end-to-end delay is only 190 ms.
Keywords: Trust mechanism, evaluation mechanism, community, opportunistic social networks
