A swarm intelligence learning model of adaptive incentive protocols for P2P networks

Abstract

Incentive protocols are critical for promoting contribution and cooperation among peers in P2P networks. The behaviour of peers has a significant impact on the effects of incentive protocols. Inspired by the biological systems, a swarm intelligence learning model of adaptive incentive protocols is proposed for P2P networks. The learning model is designed by having peers as particles in the moving swarm. The learning and adaption of peers are guided by the current best strategy as well as the best strategy in history. Simulation results demonstrate that the proposed learning model has a faster convergence rate towards at least the quasi-optimum than the two existing learning models.