Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics

Abstract

In the convergence of the Cyber-Physical World, user devices will act as proxies of the humans in the cyber world. They will be required to act in a vast information landscape, asserting the relevance of data spread in the cyber world, in order to let their human users become aware of the content they really need. This is a remarkably similar situation to what the human brain has to do all the time when deciding what information coming from the surrounding environment is interesting and what can simply be ignored. The brain performs this task using so called cognitive heuristics, i.e. simple, rapid, yet very effective schemes. In this article, we propose a new approach that exploits one of these heuristics, the recognition heuristic, for developing a self-adaptive system that deals with effective data dissemination in opportunistic networks. We show how to implement it and provide an extensive analysis via simulation. Specifically, results show that the proposed solution is as effective as state-of-the-art solutions for data dissemination in opportunistic networks, while requiring far less resources. Finally, our sensitiveness analysis shows how various parameters depend on the context where nodes are situated, and suggest corresponding optimal configurations for the algorithm.