  In cyber-physical convergence scenarios information flows seamlessly between
the physical and the cyber worlds. Here, users' mobile devices represent a
natural bridge through which users process acquired information and perform
actions. The sheer amount of data available in this context calls for novel,
autonomous and lightweight data-filtering solutions, where only relevant
information is finally presented to users. Moreover, in many real-world
scenarios data is not categorised in predefined topics, but it is generally
accompanied by semantic descriptions possibly describing users' interests. In
these complex conditions, user devices should autonomously become aware not
only of the existence of data in the network, but also of their semantic
descriptions and correlations between them. To tackle these issues, we present
a set of algorithms for knowledge and data dissemination in opportunistic
networks, based on simple and very effective models (called cognitive
heuristics) coming from cognitive sciences. We show how to exploit them to
disseminate both semantic data and the corresponding data items. We provide a
thorough performance analysis, under various different conditions comparing our
results against non-cognitive solutions. Simulation results demonstrate the
superior performance of our solution towards a more effective semantic
knowledge acquisition and representation, and a more tailored content
acquisition.
