Novel data transmission technology based on complex IoT system in opportunistic social networks.

With the development and maturity of 5G communication technology, the intelligent terminals and applications in the Internet of Things have exploded. The large-capacity data generated by the Internet of Things has higher transmission requirements on the network. Opportunistic social networks use the "store-carry-forward" method to complete message delivery, so it is particularly important to select reliable relay users to ensure the integrity of data transmission. However, users have limited memory resources. A large number of redundant message copies in the network will lead to increased node energy consumption, which will further cause network congestion. In addition, the user's movement is random and dynamic. Frequent changes in the network topology may lead to transmission link interruption and data loss. Based on the characteristics of social networks, this paper proposes a clustered dataset transmission strategy in opportunistic social networks based on the probability of encounters between users and the characteristics of user motion (PC-OSN). First, the method divides users into different clusters according to the strength of the relationship between users, which effectively reduces the occupation of network resources when users are clustered. Then PC-OSN adopts different message forwarding judgment indicators within and between clusters. PC-OSN forwards messages in a cluster by using binary injection to ensure that messages are delivered to users with a high probability of encountering. The user's activity level and motion characteristics are considered comprehensively among the clusters to determine the user's priority so as to deliver the message to the reliable user. PC-OSN fully considers the characteristics of users and the correlation between users, reducing the blindness of message forwarding and waste of ineffective resources. Experimental results show that PC-OSN achieves a message delivery rate of 0.94, which is about 14% higher than other methods. Its average latency and network overhead are reduced by about 24% and 16%, respectively. [ABSTRACT FROM AUTHOR]