On Social Delay-Tolerant Networking: Aggregation, Tie Detection, and Routing

Social-based routing protocols have shown their promising capability to improve the message delivery efficiency in Delay Tolerant Networks (DTNs). The efficiency greatly relies on the quality of the aggregated social graph that is determined by the metrics used to measure the strength of social connections. In this paper, we propose an improved metrics that leads to high-quality social graph by taking both frequency and duration of contacts into consideration. Furthermore, to improve the performance of social-based message transmission, we systematically study the community evolution problem that has been little investigated in the literation. Distributed algorithms based on the obtained social graph are developed such that the overlapping communities and bridge nodes (i.e., connecting nodes between communities) can be dynamically detected in an evolutionary social network. Finally, we take all the results above into our social-based routing design. Extensive trace-driven simulation results show that our routing algorithm outperforms existing social-based forwarding strategies significantly.