A Hybrid Artificial Neural Network for Task Offloading in Mobile Edge Computing

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

4 Scopus citations

Abstract

Edge Computing (EC) is about remodeling the way data is handled, processed, and delivered within a vast heterogeneous network. One of the fundamental concepts of EC is to push the data processing near the edge by exploiting front-end devices with powerful computation capabilities. Thus, limiting the use of centralized architecture, such as cloud computing, to only when it is necessary. This paper proposes a novel edge computer offloading technique that assigns computational tasks generated by devices to potential edge computers with enough computational resources. The proposed approach clusters the edge computers based on their hardware specifications. Afterwards, the tasks generated by devices will be fed to a hybrid Artificial Neural Network (ANN) model that predicts, based on these tasks, the profiles, i.e., features, of the edge computers with enough computational resources to execute them. The predicted edge computers are then assigned to the cluster they belong to so that each task is assigned to a cluster of edge computers. Finally, we choose for each task the edge computer that is expected to provide the fastest response time. The experiment results show that our proposed approach outperforms other state-of-the-art machine learning approaches using real-world IoT dataset.

Original languageEnglish (US)
Title of host publicationMWSCAS 2022 - 65th IEEE International Midwest Symposium on Circuits and Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665402798
DOIs
StatePublished - 2022
Event65th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2022 - Fukuoka, Japan
Duration: Aug 7 2022Aug 10 2022

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2022-August
ISSN (Print)1548-3746

Conference

Conference65th IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2022
Country/TerritoryJapan
CityFukuoka
Period08/7/2208/10/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • edge computing
  • Internet of Things (IoT)
  • machine learning
  • resource allocation
  • task offloading

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A Hybrid Artificial Neural Network for Task Offloading in Mobile Edge Computing'. Together they form a unique fingerprint.

Cite this