Perspectives of Digital Twin-Empowered Distributed Artificial Intelligence for Edge Computing
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hoa Tran-Dang | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.date.accessioned | 2024-07-17T08:00:44Z | - |
dc.date.available | 2024-07-17T08:00:44Z | - |
dc.date.issued | 2023-09 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28789 | - |
dc.description.abstract | Driven by 6G networking, Edge Intelligence (EI) makes the most of the widespread edge resources to gain Artificial Intelligence (AI) insight. Future time-critical and data-intensive applications need distributed AI (DAI) and analytics solutions on the Edge computing platforms to enable EI from small devices to whole industrial factories. To deal with critical challenges of DAI implementation such as communication reliability, resource constrains and heterogeneity of edge devices, and dynamic nature of edge computing environment, we integrate digital twin (DT) technology to form an efficient framework. With this framework, efficient DT models are developed for edge devices, edge servers, and edge networks to predict accurately the states of physical entities using probabilistic graphical models (PGMs) and machine learning (ML) algorithms. In addition, a DT-empowered edge computing architecture specifying the optimal edge server placement, DT placement, and edge clustering is developed to support the implementation and development of DAI solutions. Specially, the framework is able to support various task partitioning-based DAI models including data parallelism, model parallelism, and pipeline parallelism as well as federated learning. | - |
dc.format.extent | 4 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | Perspectives of Digital Twin-Empowered Distributed Artificial Intelligence for Edge Computing | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ICAII59460.2023.10497436 | - |
dc.identifier.scopusid | 2-s2.0-85191901438 | - |
dc.identifier.wosid | 001213931400012 | - |
dc.identifier.bibliographicCitation | 2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE INNOVATION, ICAII 2023, pp 72 - 75 | - |
dc.citation.title | 2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE INNOVATION, ICAII 2023 | - |
dc.citation.startPage | 72 | - |
dc.citation.endPage | 75 | - |
dc.type.docType | Proceedings Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.subject.keywordAuthor | edge computing | - |
dc.subject.keywordAuthor | digital twin | - |
dc.subject.keywordAuthor | distributed AI | - |
dc.subject.keywordAuthor | edge intelligence | - |
dc.subject.keywordAuthor | Industrial IoT | - |
dc.subject.keywordAuthor | 6G networking | - |
dc.subject.keywordAuthor | Probabilistic Graphical Models (PGMs) | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.