Towards Content Distribution in Opportunistic Networks

Opportunistic networking is a new communication paradigm. Content Distribution in opportunistic networks is challenging due to intermittent connectivity, short connection durations and a highly dynamic topology. Research is needed to develop new applications and protocols that can distribute content in opportunistic networks. This thesis explores and evaluates approaches to developing mobile P2P systems for content distribution, focusing mainly on the problem of modeling device contacts. Contact duration and patterns of connections influence routing and forwarding strategies. To model device contacts we need to capture the characteristics of the network and be able to model its behavior. Connectivity models capture the aggregated network behavior by modeling the social connectedness of the network. A model of inter-device relationships can be constructed using parameters are extracted from connectivity traces collected in the field using real devices. These traces represent how connectivity might look in an opportunistic network. Altering and fine tuning these parameters enables us to change the stochastic behavior of the network and study the behavior of applications and protocols. Another approach is mobility modeling. There are two major drawbacks to using mobility models. First, in ad hoc networks traces have been collected which estimate the connectivity of the network. Typically traces are then used to model node mobility which in turn generates nodal connectivity during a simulation. This is a wasteful process and as the network size grows it becomes a tedious job. Second, the inter-contact time distribution observed in traces differs greatly from what is generated by popular mobility models. We have developed a connectivity model to generate synthetic device contact traces for opportunistic networks. We present the preliminary validation results from a comparative study of synthetic traces and traces collected in the field.