Understanding Uncertainty of Edge Computing: New Principle and Design Approach

Sejin Seo, Seung Woo Ko, Sujin Kook, Seong Lyun Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Due to the edge's position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately. Particularly, the edge users' locally specific requirements that change depending on time and location cause a phenomenon called dataset shift, defined as the difference between the training and test datasets' representations. It renders many of the state-of-the-art approaches for resolving uncertainty insufficient. Instead of finding ways around it, we exploit such phenomenon by utilizing a new principle: AI model diversity, which is achieved when the user is allowed to opportunistically choose from multiple AI models. To utilize AI model diversity, we propose Model Diversity Network (MoDNet), and provide design guidelines and future directions for efficient learning driven communication schemes.

Original languageEnglish
Title of host publication2022 IEEE 95th Vehicular Technology Conference - Spring, VTC 2022-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665482431
Publication statusPublished - 2022
Event95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring - Helsinki, Finland
Duration: 2022 Jun 192022 Jun 22

Publication series

NameIEEE Vehicular Technology Conference
ISSN (Print)1550-2252


Conference95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring

Bibliographical note

Funding Information:
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by

Publisher Copyright:
© 2022 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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