A Bayesian Approach to Improve the Performance of P2P Networks

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 27)


The task of efficient peer-to-peer information retrieval has been one of the most serious challenges in the history of information technology. Since the computing resources and increased usability of smartphones make these devices with their high proliferation a suitable platform for presenting different kinds of information, it becomes highly important to integrate them into the peer-to-peer (P2P) information retrieval world. There is an increasing need for mobile software that can locate and retrieve well-defined documents (texts, music, or video files) that are in the fields of interest of the mobile user. There are different proposals to make P2P information retrieval more efficient using semantic data; however, the application of these protocols in the mobile environment is not straightforward, because they generate higher network traffic and require longer online time for the participants. However, because of various considerations, such as the limited connectivity of these devices, they do not spend much time connected to the network. The Bayesian approach presented in this paper targets this strong transient character of mobile P2P clients. We will show how the network topology can be quickly improved to increase the hit rate with an appropriate protocol and algorithm using a Bayesian process based on local decisions derived from the fields of interest of the nodes.


Network Traffic Mobile Environment Candidate Node Protocol Extension Semantic Connection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Automation and Applied InformaticsBudapest University of Technology and EconomicsHungary

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