Modeling opportunistic communication with churn

In opportunistic networking, characterizing contact patterns between mobile users is essential for assessing feasibility and performance of opportunistic applications. There has been significant efforts in deriving this characterization, based on observations and trace analyses; however, most of the previously established results were obtained by studying contact opportunities at large spatial and temporal scales. Moreover, the user population is considered to be constant: no user can join or leave the system. Yet, there are many examples of scenarios which do not fully adhere to the previous assumption and cannot be accurately described at large scales. Urban environments, such as smaller city districts, are characterized by highly dynamic user populations. We believe that scenarios with varying population require further investigation. In this paper, we present a novel modeling approach to study operation of opportunistic applications in scenarios where the population size is subjected to frequent changes, that is, it exhibits churn. We examine two location-based content sharing schemes: a purely opportunistic case and an infrastructure-supported content sharing scheme, for which we provide stochastic models based on stochastic differential equations (SDEs). We validate our models in five scenarios: a city area, subway station, conference, campus, and a scenario with a synthetic mobility model and we show that the models provide good representations of the investigated scenarios.