We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.
Another relevant area deals with modeling data dissemination in opportunistic networks. In Ref. @cite_2 data is proactively disseminated using a strategy based on the utility of the data itself. Utility is defined on top of existing social relationships between users, and the resulting Markovian model is validated in simulation only.
@cite_2
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In opportunistic networks data dissemination is an important, although not widely explored, topic. Since opportunistic networks topologies are very challenged and unstable, data-centric approaches are an interesting direction to pursue. Data should be proactively and cooperatively disseminated from sources towards possibly interested receivers, as sources and receivers might not be aware of each other, and never get in touch directly. In this paper we consider a utility-based cooperative data dissemination system in which the utility of data is defined based on the social relationships between users. Specifically, we study the performance of this system through an analytical model. Our model allows us to completely characterise the data dissemination process, as it describes both its stationary and transient regimes. After validating the model, we study the system's behaviour with respect to key parameters such as the definition of the data utility function, the initial data allocation on nodes, the number of users in the system, and the data popularity.