D2D Based Advertisement Dissemination Using Expectation Maximization Clustering

기대최대화 기반 사용자 클러스터링을 통한 D2D 광고 확산

  • Kim, Junseon (Ulsan Institute of Science and Technology (UNIST) Department of Computer Science Engineering (CSE)) ;
  • Lee, Howon (Hankyong National University Department of Electrical, Electronic and Control Engineering (EECE) and IITC)
  • Received : 2016.08.01
  • Accepted : 2017.04.18
  • Published : 2017.05.31


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.


Supported by : 한국연구재단


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