Enhancing Data Forwarding Efficiency in SIoT with Multidimensional Social Relations

Effective data communication is a crucial aspect of the Social Internet of Things (SIoT) and continues to be a significant research focus. This paper proposes a data forwarding algorithm based on Multidimensional Social Relations (MSRR) in SIoT to solve this problem. The proposed algorithm separates message forwarding into intra- and cross-community forwarding by analyzing interest traits and social connections among nodes. Three new metrics are defined: the intensity of node social relationships, node activity, and community connectivity. Within the community, messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity. When a node performs cross-community forwarding, the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities. The proposed algorithm was compared to three existing routing algorithms in simulation experiments. Results indicate that the proposed algorithm substantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context.