A P2P Reputation Incentive Mechanism based on Artificial Fish-Swarm Model

Recently, P2P networks play an important role in network. However, there exist a large amount of malicious actions in P2P networks resulting from the lack of reputation system. For example, nodes are willing to share resources each other and may withdraw from networks at any time. Thus, the system is difficult to guarantee effectiveness and availability. According to the behavior of fish school, A novel optimizing, Fish-swam Algorithm, was proposed in this paper, which can decide the next step when nodes have no interactive experience with others. The algorithm based on the model of autonoms animals and was presented to be a kind of efficient incentive mechanism, through a series of experimental study.