Data dissemination in opportunistic networks using cognitive heuristics

It is often argued that the Future Internet will be a very large scale content-centric network. Scalability issues will stem even more from the amount of content nodes will generate, share and consume. In order to let users become aware and retrieve the content they really need, these nodes will be required to swiftly react to stimuli and assert the relevance of discovered data under uncertainty and only partial information. The human brain performs the task of information filtering and selection using the so-called cognitive heuristics, i.e. simple, rapid, low-resource demanding, yet very effective schemes that can be modeled using a functional approach. In this paper we propose a solution based on one such heuristics, namely the recognition heuristic, for dealing with data dissemination in opportunistic networks. We show how to model an algorithm that exploits the environmental information in order to implement an effective dissemination of data based on the recognition heuristic, and provide a performance evaluation of such a solution via simulation