Autonomic cognitive-based data dissemination in Opportunistic Networks

Abstract

Opportunistic Networks (OppNets) offer a very volatile and dynamic networking environment. Several applications proposed for OppNets - such as social networking, emergency management, pervasive and urban sensing - involve the problem of sharing content amongst interested users. Despite the fact that nodes have limited resources, existing solutions for content sharing require that the nodes maintain and exchange large amount of status information, but this limits the system scalability. In order to cope with this problem, in this paper we present and evaluate a solution based on cognitive heuristics. Cognitive heuristics are functional models of the mental processes, studied in the cognitive psychology field. They describe the behavior of the brain when decisions have to be taken quickly, in spite of incomplete information. In our solution, nodes maintain an aggregated information built up from observations of the encountered nodes. The aggregate status and a probabilistic decision process is the basis on which nodes apply cognitive heuristics to decide how to disseminate content items upon meeting with each other. These two features allow the proposed solution to drastically limit the state kept by each node, and to dynamically adapt to both the dynamics of item diffusion and the dynamically changing node interests. The performance of our solution is evaluated through simulation and compared with other solutions in the literature.