A Robust Unstructured Mobile Peer-to-Peer Files Sharing System under Higher Peer Churn Rate

In the unstructured Peer-to-Peer systems, a core operation is efficient location of resources. Conventional informative searching algorithms, however, always cannot perform well under peer churn rate network environments. In this paper, we designed a robust unstructured peer-to-peer files sharing system. When forwarding query, we usually choose the peer with the highest forward probability. But in this paper we didn't compute the forward probability according to the current neighbor node's individual search history but to a leaning model based on machine learning technologies. We proposed a more robust way for searching. The experimental results show that our methods are more effective and efficient under higher peer churn environment.