Time-dependent smart data pricing based on machine learning

Yi Chia Tsai*, Yu Da Cheng, Cheng Wei Wu, Yueh Ting Lai, Wan Hsun Hu, Jeu Yih Jeng, Yu-Chee Tseng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


The purpose of time-dependent smart data pricing (abbreviated as TDP) is to relieve network congestion by offering network users different prices over varied periods. However, traditional TDP has not considered applying machine learning concepts in determining prices. In this paper, we propose a new framework for TDP based on machine learning concepts. We propose two different pricing algorithms, named TDP-TR (TDP based on Transition Rules) and TDP-KNN (TDP based on K-Nearest Neighbors). TDP-TR determines prices based on users’ past willingness to pay given different prices, while TDP-KNN determines prices based on the similarity of users’ past network usages. The main merit of TDP-TR is low computational cost, while that of TDP-KNN is low maintenance cost. Experimental results on simulated datasets show that the proposed algorithms have good performance and profitability.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence - 30th Canadian Conference on Artificial Intelligence, Canadian AI 2017, Proceedings
EditorsPhilippe Langlais, Malek Mouhoub
PublisherSpringer Verlag
Number of pages6
ISBN (Print)9783319573502
StatePublished - 1 Jan 2017
Event30th Canadian Conference on Artificial Intelligence, AI 2017 - Edmonton, Canada
Duration: 16 May 201719 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10233 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th Canadian Conference on Artificial Intelligence, AI 2017


  • K-nearest neighbour
  • Machine learning
  • Network congestion management
  • Smart data pricing

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