SSR: Using the Social Similarity to Improve the Data Forwarding Performance in Mobile Opportunistic Networks

Two nodes with similar social behavior mean that they contact each other frequently. Therefore, forwarding packets to them can efficiently improve the performance of routing algorithms in mobile opportunistic networks (MONs). Previous works use different factors to evaluate the similarity between nodes. However, they neglect the importance of nodes, which has a big influence on the system performance. How to use social importance to compute node similarity is still an open issue in MONs. This paper proposes SSR, a social similarity-based routing algorithm combining the context of social importance. First, nodes record the social importance of encounters in their buffer. Second, whenever two nodes are in contact, a dynamic time warping algorithm is used to determine the similarity of the two nodes' social importance sequences. The more similar the two sequences are, the more similar the social behavior of the two nodes is. Finally, the packet is always forwarded to the relay node with the most similar social behavior to the destination node to ensure the delivery efficiency. The simulation results show that compared with the traditional social routing algorithm, SSR significantly improves the packet delivery rate and reduces the delivery delay, cost, and hops.