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P2P Network for very large virtual environment

Published: 01 November 2006 Publication History

Abstract

The ever increasing speed of Internet connections has led to a point where it is actually possible for every end user to seamlessly share data on Internet. Peer-To-Peer (P2P) networks are typical of this evolution. The goal of our paper is to show that server-less P2P networks with self-adaptive assignment techniques can efficiently deal with very large environments such as met in the geovisualization domain. Our method allows adaptative view-dependent visualization thanks to a hierarchical and progressive data structure that describes the environment. In order to assess the global efficiency of this P2P technique, we have implemented a dedicated real time simulator. Experimentation results are presented using a hierarchical LOD model of a very large urban environment.

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cover image ACM Conferences
VRST '06: Proceedings of the ACM symposium on Virtual reality software and technology
November 2006
400 pages
ISBN:1595933212
DOI:10.1145/1180495
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 November 2006

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Author Tags

  1. peer-to-peer
  2. self-adaptation
  3. self-organization
  4. self-scalability
  5. simulation
  6. virtual environment

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Overall Acceptance Rate 66 of 254 submissions, 26%

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  • (2013)Hybrid P2P schemes for remote terrain interactive visualization systemsFuture Generation Computer Systems10.1016/j.future.2012.11.00229:6(1522-1532)Online publication date: 1-Aug-2013
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