An incentive mechanism based on the Bayes Equilibrium of game theory in peer-to-peer networks

The free-riding problem is widely accepted to be retarding the deployment and growth of P2P networks. In order to solve this problem, we give a strict definition of the peer´s contribution, utility and incentive reputation and model a P2P network with heterogeneous peers as an incomplete information static game. We use incentive reputation to incentivize peers to share their resources and provide services to others, for the services available to the peer are directly depend on its current incentive reputation. A peer can enhance its reputation by increasing its contribution. Experimental results show that all peers optimize their strategy to the Bayes Equilibrium in which the utility of each peer´s is maximized and the free-riding problem is minimized, then the whole network serves at a high quality.