The existing literature deals with the problems dealing with decentralized content-based P2P community formation and community-based search separately. Contrary to this approach, in this paper we propose a novel search algorithm that has both the capability to form the community structure as well as search it with maximum efficiency. The key contribution is to design a self-organized and adaptive search algorithm where as the community topology evolves with time, the search process adapts automatically to the situation to provide best performance. It performs an automatic transition from the exploratory phase to search phase, by estimating the global state of communities using a local control parameter. Moreover, we show that the strategy is also robust enough to improve search performance even under node churn, though a graceful degradation in overall performance is seen. We consider realistic power-law distribution for node degrees and information profiles. The proposed search strategy shows more than twice efficiency than a pure random walk with proliferation on the same network.

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In: in Proceedings of the 3rd Workshop on Intelligent Networks: Adaptation, Communication & Reconfiguration (IAMCOM) 2009

Publisher: IEEE Computer Society
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