The Identification of Secular Variation in IoT Based on Transfer Learning

Caidan Zhao, Zhibiao Cai, Minmin Huang, Mingxian Shi, Xiaojiang Du, Mohsen Guizani

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

10 Scopus citations

Abstract

In the Internet of Things(IoT) equipment, the characteristic space of the physical layer has changed slightly due to prolongation of the use time and the change of the environment, which may result to the terrible identification of the new target. To solve the problem, this paper uses transfer learning to update the instance weights and combines the weight with rejection sampling to construct the training set. This method provides a black box for transfer learning and a possibility for building multi-classification transfer learning. Some experimental results show that the rate can increase 10% when the number of target samples is too small to train a new learning model.

Original languageEnglish
Title of host publication2018 International Conference on Computing, Networking and Communications, ICNC 2018
Pages878-882
Number of pages5
ISBN (Electronic)9781538636527
DOIs
StatePublished - 19 Jun 2018
Event2018 International Conference on Computing, Networking and Communications, ICNC 2018 - Maui, United States
Duration: 5 Mar 20188 Mar 2018

Publication series

Name2018 International Conference on Computing, Networking and Communications, ICNC 2018

Conference

Conference2018 International Conference on Computing, Networking and Communications, ICNC 2018
Country/TerritoryUnited States
CityMaui
Period5/03/188/03/18

Keywords

  • Internet of Things
  • multi-classification
  • rejection sampling
  • transfer learning

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