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Prototype of an Architecture for Object Resolution Services in Information-Centric Environment

Published: 30 September 2015 Publication History

Abstract

In this demo, we implemented a prototype of the our recent work ORICE -- an architecture for Object Resolution services in Information-Centric Environment that maps user inputs to Content Names. With demonstration, we show the feasibility of ORICE in fulfilling the necessity for object resolution services in ICN. We will deploy the ICN platform in our lab test bed and the front-end application will access the network through SSH tunnels.

References

[1]
V. Jacobson et al., "Networking Named Content," in CoNEXT, 2009.
[2]
J. Chen et al., "COPSS: An Efficient Content Oriented Publish/Subscribe System," in ANCS, 2011.
[3]
S. S. Adhatarao et al., "ORICE: an Architecture for Object Resolution Services in Information-Centric Environment," in LANMAN, 2015.

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  1. Prototype of an Architecture for Object Resolution Services in Information-Centric Environment

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    cover image ACM Conferences
    ACM-ICN '15: Proceedings of the 2nd ACM Conference on Information-Centric Networking
    September 2015
    236 pages
    ISBN:9781450338554
    DOI:10.1145/2810156
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 30 September 2015

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    Author Tags

    1. ICN
    2. object resolution
    3. search engine

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    • Demonstration

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    ICN'15
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    ICN'15: 2nd International Conference on Information-Centric Networking
    September 30 - October 2, 2015
    California, San Francisco, USA

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    ACM-ICN '15 Paper Acceptance Rate 18 of 55 submissions, 33%;
    Overall Acceptance Rate 133 of 482 submissions, 28%

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