D2D Based Advertisement Dissemination Using Expectation Maximization Clustering 


Vol. 42,  No. 5, pp. 992-998, May  2017


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  Abstract

For local advertising based on D2D communications, sources want advertisement messages to be diffused to unspecified users as many as possible. It is one of challenging issues to select target-areas for advertising if users are uniformly distributed. In this paper, we propose D2D based advertisement dissemination algorithm using user clustering with expectation-maximization. The user distribution of each cluster can be estimated by principal components (PCs) obtained from each cluster. That is, PCs enable the target-areas and routing paths to be properly determined according to the user distribution. Consequently, advertisement messages are able to be disseminated to many users. We evaluate performances of our proposed algorithm with respect to coverage probability and average reception number per user.

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  Cite this article

[IEEE Style]

J. Kim and H. Lee, "D2D Based Advertisement Dissemination Using Expectation Maximization Clustering," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 5, pp. 992-998, 2017. DOI: .

[ACM Style]

Junseon Kim and Howon Lee. 2017. D2D Based Advertisement Dissemination Using Expectation Maximization Clustering. The Journal of Korean Institute of Communications and Information Sciences, 42, 5, (2017), 992-998. DOI: .

[KICS Style]

Junseon Kim and Howon Lee, "D2D Based Advertisement Dissemination Using Expectation Maximization Clustering," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 5, pp. 992-998, 5. 2017.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880