Contact graph based routing in opportunistic networks

Opportunistic networking targets networks where end-to-end communication paths between nodes may not exist and it becomes important to seize opportunities to forward data to neighbouring nodes in the hope that it will eventually be propagated to the destination. Forwarding algorithms must address the trade-off between delivery performance (success ratio, end-to-end delay) and resource consumption (memory and bandwidth). Incorporating information about the contact pattern between nodes helps to distinguish nodes based on their forwarding capabilities. Aggregating contact events into weighted graphs, and thus forming a contact graph, enables us to detect communities and learn about the neighbourhoods of nodes. In this paper we incorporate this learning into a contact graph-based forwarding algorithm that makes decisions based on network communities. We compare the performance of this algorithm to existing forwarding schemes using simulations built on two real word contact-trace datasets that have distinct characteristics.