Social Network Analysis for Information Flow in Disconnected Delay-Tolerant MANETs

Message delivery in sparse mobile ad hoc networks (MANETs) is difficult due to the fact that the network graph is rarely (if ever) connected. A key challenge is to find a route that can provide good delivery performance and low end-to-end delay in a disconnected network graph where nodes may move freely. We cast this challenge as an information flow problem in a social network. This paper presents social network analysis metrics that may be used to support a novel and practical forwarding solution to provide efficient message delivery in disconnected delay-tolerant MANETs. These metrics are based on social analysis of a node's past interactions and consists of three locally evaluated components: a node's "betweenness" centrality (calculated using ego networks) and a node's social 'similarity' to the destination node and a node's tie strength relationship with the destination node. We present simulations using three real trace data sets to demonstrate that by combining these metrics delivery performance may be achieved close to epidemic routing but with significantly reduced overhead. Additionally, we show improved performance when compared to PRoPHET routing.