Efficient social-aware content placement in opportunistic networks

As content provisioning becomes the driving application of today's (opportunistic) networking environments and the User Generated Content explodes, the problem of devising scalable approaches to placing it optimally within a networking structure becomes more important and challenging. Since the well-known k-median optimization problem that is typically formulated to address it requires global topology and demand information, different approaches are sought for. The latter is the focus of this paper that aims at exploiting social structures, present in emerging networking environments, in order to devise a scalable approach to the optimal or near-optimal content placement. A new metric that captures the node's social significance or potential for helping establish paths between nodes is introduced and serves as the basis for creating a small scale network sub-graph over which the small-scale content placement problem is solved sequentially until the optimal or near-optimal location is identified. The trade-off between the sub-graph's size and the degree of convergence to the optimal solution is studied through simulations on E-R and B-A random graphs and the effectiveness of the proposed approach is demonstrated.