Learning Automata for Cognitive Peer-to-Peer Networks

Abstract

In this chapter, we present an approach for designing cognitive peer-to-peer networks based on DCLAs. In this chapter, several cognitive engines based on the DCLAs are presented for solving topology mismatch, and super-peer selection problems in peer-to-peer networks. In order to design the structure updating rule of the DCLAs, the restructuring rules of Schelling segregation model, fungal growth model, and Voronoi diagrams model were borrowed. To evaluate the proposed cognitive peer-to-peer networks several experiments have been conducted. Experimentations have shown that the proposed cognitive peer-to-peer networks for solving topology mismatch problem perform better than the existing algorithms with respect to communication delay. Also, experimentations have shown the superiority of the proposed cognitive peer-to-peer networks for solving super-peer selection problem over the existing algorithms with respect to capacity utilization and number of super-peers.