Incentive Mechanism for P2P Networks Based on Markov Chain

Research Article
Incentive Mechanism for P2P Networks Based on Markov Chain

Hongwei Chen, Hui Xu, Chunzhi Wang and Ke Zhou

Information Technology Journal, 2011, 10(12), 2242-2251.


Due to node’s behavioral analysis, grasping the inertia psychology of its behavior choice and considering whole influences of the network such as group selection, this study establishes the forecast mechanism in P2P network. In the process of network running and node communication, this mechanism carries on real-time surveillance to all nodes, according to the differences of network states and takes the corresponding incentive and penalty measure to the node, thus drives node well serve for the P2P network. This mechanism uses the Markov chain to forecast future development state of the network, combines the forecast of future node state shift situation and adopts more prompt and more effective measure to the network ahead of time. Summarizing the types of network state, each network state mechanism will have a correspond model which will carry on the drive to the node behavior.

ASCI-ID: 28-1463

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