Collaboration Incenting Reputation Model Based on Repeated Game Theory and Punishment Mechanism in P2P Networks

To deal with the shortcomings in high time complexity and network overhead in calculating nodes' trust degree,as well as to take into account the impact of rational nodes ratio and nodes patience degree on the trust model,the punishment mechanism is utilized to establish the P2P trust model based on the repeated game theory.The trust mechanism is described and the repeated trade actions are analyzed by using repeated game theory.The collaborative equilibrium theorem considering the rational nodes ratio and nodes patience degree is proposed and proved,from which a collaboration condition is concluded and the nodes should adopt the collaboration strategy for fear of being punished under this condition.The experimental results show the effectiveness of this trust model in promoting the nodes collaboration.