Avoiding Congestion in a Data-Centric Opportunistic Network

In opportunistic networking, data delivery is achieved by replicating data when transmitted to nodes within communication reach. The process repeats on a hop by hop basis, forwarding the data closer to other communities that have an interest in the given data. The replication and the unpredictable contact times due to mobility, requires buffer management strategies to avoid the nodes from experiencing buffer overflow. This thesis investigates buffer management strategies based on local forwarding statistics and the relevance of the data for other nodes. Using our emulation platform for opportunistic networks, the obtained results shows that strategies with a high data refresh rate achieve the most efficient delivery and generate the smallest overhead on our community and mobility scenarios. As a decade has passed since this research was completed, we retrospectively look at the research field to view how the work can be leveraged given the new architectures that are in development in current Information-centric Networking.