SocP2P: P2P content discovery enhancement by considering social networks characteristics

Reza Farahbakhsh, Noel Crespi, Angel Cuevas, Sraddha Adhikari, Mehdi Mani, Teerapat Sanguankotchakorn

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Content management appears as an essential requirement in order to deploy enhanced P2P networks. In P2P networks, the content that is requested by the query node could be located at different locations/nodes; therefore an efficient search mechanism is required. The proposed search algorithm in this paper, called socP2P, relies on peers' social relationships (friendships, shared interests, shared background and experiences) to improve the content discovery compared to similar solutions. With socP2P nodes can improve searches by using knowledge gained by overheard information during their stay in the network. In addition, our algorithm exploits peers' common interests, friendships, and capability of memorizing experiences (received and routed queries) by them. Simulation results show that socP2P is able to achieve a high success rate, low delay and low overhead. We have verified that our algorithm is not only useful in finding popular contents in the network but also good enough to locate rare files. The obtained results reflect that exploiting social information in P2P networks leads to a more efficient content search mechanism.

Original languageEnglish
Title of host publication2012 IEEE Symposium on Computers and Communications, ISCC 2012
Pages530-533
Number of pages4
DOIs
Publication statusPublished - 28 Sept 2012
Externally publishedYes
Event17th IEEE Symposium on Computers and Communication, ISCC 2012 - Cappadocia, Turkey
Duration: 1 Jul 20124 Jul 2012

Publication series

NameProceedings - IEEE Symposium on Computers and Communications
ISSN (Print)1530-1346

Conference

Conference17th IEEE Symposium on Computers and Communication, ISCC 2012
Country/TerritoryTurkey
CityCappadocia
Period1/07/124/07/12

Keywords

  • Content Discovery
  • Peer-to-Peer networks
  • Random Walk
  • Social Network

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