Community Clustering Routing Algorithm Based on Information Entropy in Mobile Opportunity Network

Aiming at the difficulty of traditional routing clustering algorithms to deal with the different characteristics between communities and the inefficient nodes after community clustering, this paper proposes a Community clustering Routing protocol based on information Entropy in mobile opportunity Networks(CREN). The proposed protocol uses the K-Modes algorithm with unsupervised learning, combined with the pre-selected initial clustering center node to divide the network nodes into the initial clustering community. Then, the communities with similar characteristics are clustered and merged according to the change of information entropy. At the end, a number of different types of communities are formed in the network, and the nodes in the community have a high degree of similarity, which improves the efficiency of message forwarding. At the same time, in order to eliminate the inefficient nodes in the community, based on the information entropy and the social attributes of the nodes, this paper proposes a mechanism for dynamically updating the community to ensure the efficiency of the nodes in the community. The simulation results show that the transmission success rate of this algorithm is better than other classic routing algorithms, meanwhile, it also has lower transmission delay and routing overhead.