Effective communication data transmission based on community clustering in opportunistic social networks in IoT system
Xiangxiang Zhang, Chang Liu, Jingwen Luo, Jia Wu
Abstract
With the rise of the Internet of Things, the opportunistic network of portable smart devices has become a new hot spot in academic research in recent years. The mobility of nodes in opportunistic networks makes the communication links between nodes unstable, so data forwarding is an important research content in opportunistic networks. However, the traditional opportunistic network algorithm only considers the transmission of information and does not consider the social relationship between people, resulting in a low transmission rate and high network overhead. Therefore, this paper proposes an efficient data transmission model based on community clustering. According to the user’s social relationship and the release location of the points of interest, the nodes with a high degree of interest relevance are divided into the same community. Weaken the concept of a central point in the community, and users can share information to solve the problem of excessive load on some nodes in the network and sizeable end-to-end delay.