Socially-aware content retrieval using random walks in Disruption Tolerant Networks

In this paper, we propose a distributed content retrieval scheme for Disruption Tolerant Networks (DTNs). Our scheme consists of two key components: a content discovery (lookup) service and a routing protocol for message delivery. Both components rely on three key social metrics: centrality, social level, and social tie. Centrality guides the placement of the content lookup service. Social level guides the forwarding of content requests to a content lookup service node. Social tie is exploited to deliver content requests to the content provider, and content data to the requester node. We leverage bounded random walks to estimate a node's centrality. The X-means clustering algorithm is used to compute a node's social level. Lastly, a node's social tie is computed based on the frequency and recency of node contacts. Extensive real-trace-driven simulation results show that our scheme requires less control overhead while maintaining comparable performance for content retrieval applications.