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Content distribution in Information Centric Network: Economic incentive analysis in game theoretic approach

Authors: Mohammad Arifuzzaman; Takuro Sato; Keping Yu;

Content distribution in Information Centric Network: Economic incentive analysis in game theoretic approach

Abstract

The contributions of this paper are twofold. Firstly, we analyze the decision making problem of caching contents by the network players of Information Centric Networking (ICN) from a game theoretic perspective. We also mention a possible content distribution model for ICN. By our proposed game theory, different network players can find the optimality taking into consideration which benefits them with optimum revenue. Secondly, we present a solution for Live Streaming Media broadcast in ICN and analyze the economic part with a decision tree. We believe, our paper clearly shows the economic incentives for major network players which can stand as a motivation to achieve the faster accommodation of ICN architecture. Besides, we identify some standardization issue in ICN architecture and we emphasize on the need for a common standard for content routers (CR) so that as a node in the ICN, CR ensure scalable content delivery as well as its functionalities match with the Internet open standard philosophy.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
10
Average
Top 10%
Top 10%