Feature-based content dissemination process in opportunistic networks

In order to respond to drastically increased traffic demands in mobile networks such as cellulars, opportunistic communication has been introduced as a data offloading strategy. Delivering the contents using peer-to-peer data dissemination is more preferable compared to epidemic diffusion schemes. To implement peer-to-peer dissemination, one needs to define the peer selection and content dissemination policies. Efficient content dissemination relies on the selection of suitable peers. Besides that, the peers require to choose what contents to be transmitted between them. In this paper, we propose multi-level master/slave peer selection algorithm (MMSA) and variance-based multi-metric chunk selection algorithm (VMMA) which ensure efficient content dissemination with reduced signaling overhead. VMMA can use the local and global features of the contents to prioritize the contents. A mathematical analysis that considers the time varying nature of the network and traces the evolution of distribution process is provided. The simulation results demonstrate the advantages provided by these algorithms. The simulation results match the mathematical analysis.