Abstract
Finding an object in distributed peer-to-peer networks among a large volume of data needs the application of a proper intelligent search method. Designing such an intelligent search method will significantly affect the efficiency of the network taking into account sending a search query to nodes which have more probably stored the desired object. Machine learning techniques such as learning automaton can be used as an appropriate tool for this purpose. This paper tries to present a search method based on the learning automaton for the peer-to-peer networks, in which each node is selected according to values stored in its memory for sending the search queries rather than being selected randomly. The probable values are stored in tables, and they indicate the history of the node in previous searches for finding the desired object. For evaluation, simulation is used to demonstrate that the proposed algorithm outperforms K-random walk method which randomly sends the search queries to the nodes.
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Ghorbani, M., Jooyan, M., Safarpour, M. (2014). ISA: An Intelligent Search Algorithm for Peer-to-Peer Networks. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_44
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DOI: https://doi.org/10.1007/978-81-322-1665-0_44
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