Abstract
Socially-aware networking provides a promising paradigm for data forwarding by exploiting the involved nodes' social properties in mobile social networks. However, individuals' learning capability and awareness to the dynamic environment have not been well explored in the literature. In this paper, we give a brief introduction of an interest-based scheme called BEEINFO. Inspired by swarm intelligence, BEEINFO takes advantage of individuals' perceiving and learning capability to gather information of density and social tie during communication. Moreover, it classifies communities based on nodes' interests and distinguishes data forwarding into situations of inter-community and intra-community. Furthermore, BEEINFO performs efficient message scheduling and buffer management to improve performance. The simulation results show that BEEINFO outperforms PROPHET and Epidemic in message delivery ratio, overhead and hop count, except for average latency.
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