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Selective Content Retrieval in Information-Centric Networking

Recently, novel networking architectures have emerged to cope with the fast-evolving and new Internet utilisation patterns. Information-Centric Networking (ICN) is a prominent example of this architecture. By perceiving content as the core element of the networking functionalities, ICN opens up a wh...

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Detalles Bibliográficos
Autores principales: Quevedo, José, Corujo, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9698087/
https://www.ncbi.nlm.nih.gov/pubmed/36433338
http://dx.doi.org/10.3390/s22228742
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author Quevedo, José
Corujo, Daniel
author_facet Quevedo, José
Corujo, Daniel
author_sort Quevedo, José
collection PubMed
description Recently, novel networking architectures have emerged to cope with the fast-evolving and new Internet utilisation patterns. Information-Centric Networking (ICN) is a prominent example of this architecture. By perceiving content as the core element of the networking functionalities, ICN opens up a whole new avenue of information exchange optimisation possibilities. This paper presents an approach that progresses the base operation of ICN and leverages content identification right at the network layer, allowing to selectively retrieve partial pieces of information from content already present in ICN in-network caches. Additionally, this proposal enables information producers to seamlessly offload some content processing tasks into the network. The concept is discussed and demonstrated through a proof-of-concept prototype targeting an Internet of Things (IoT) scenario, where consumers retrieve specific pieces of the whole information generated by sensors. The obtained results showcase reduced traffic and storage consumption at the core of the network.
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spelling pubmed-96980872022-11-26 Selective Content Retrieval in Information-Centric Networking Quevedo, José Corujo, Daniel Sensors (Basel) Article Recently, novel networking architectures have emerged to cope with the fast-evolving and new Internet utilisation patterns. Information-Centric Networking (ICN) is a prominent example of this architecture. By perceiving content as the core element of the networking functionalities, ICN opens up a whole new avenue of information exchange optimisation possibilities. This paper presents an approach that progresses the base operation of ICN and leverages content identification right at the network layer, allowing to selectively retrieve partial pieces of information from content already present in ICN in-network caches. Additionally, this proposal enables information producers to seamlessly offload some content processing tasks into the network. The concept is discussed and demonstrated through a proof-of-concept prototype targeting an Internet of Things (IoT) scenario, where consumers retrieve specific pieces of the whole information generated by sensors. The obtained results showcase reduced traffic and storage consumption at the core of the network. MDPI 2022-11-12 /pmc/articles/PMC9698087/ /pubmed/36433338 http://dx.doi.org/10.3390/s22228742 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Quevedo, José
Corujo, Daniel
Selective Content Retrieval in Information-Centric Networking
title Selective Content Retrieval in Information-Centric Networking
title_full Selective Content Retrieval in Information-Centric Networking
title_fullStr Selective Content Retrieval in Information-Centric Networking
title_full_unstemmed Selective Content Retrieval in Information-Centric Networking
title_short Selective Content Retrieval in Information-Centric Networking
title_sort selective content retrieval in information-centric networking
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9698087/
https://www.ncbi.nlm.nih.gov/pubmed/36433338
http://dx.doi.org/10.3390/s22228742
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