Digital Twin-Native AI-Driven Service Architecture for Industrial Networks

Kübra Duran*, Matthew Broadbent*, Gökhan Yurdakul, Berk Canberk*

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

The dramatic increase in the connectivity demand results in an excessive amount of Internet of Things (IoT) sensors. To meet the management needs of these large-scale networks, such as accurate monitoring and learning capabilities, Digital Twin (DT) is the key enabler. However, current attempts regarding DT implementations remain insufficient due to the perpetual connectivity requirements of IoT networks. Furthermore, the sensor data streaming in IoT networks cause higher processing time than traditional methods. In addition to these, the current intelligent mechanisms cannot perform well due to the spatiotemporal changes in the implemented IoT network scenario. To handle these challenges, we propose a DT-native AI-driven service architecture in support of the concept of IoT networks. Within the proposed DT-native architecture, we implement a TCP-based data flow pipeline and a Reinforcement Learning (RL)-based learner model. We apply the proposed architecture to one of the broad concepts of IoT networks, the Internet of Vehicles (IoV). We measure the efficiency of our proposed architecture and note 30% processing time-saving thanks to the TCP-based data flow pipeline. Moreover, we test the performance of the learner model by applying several learning rate combinations for actor and critic networks and highlight the most successive model.

Original languageEnglish
Title of host publication2023 IEEE Globecom Workshops, GC Wkshps 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1297-1302
Number of pages6
ISBN (Electronic)9798350370218
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event2023 IEEE Globecom Workshops, GC Wkshps 2023 - Kuala Lumpur, Malaysia
Duration: 4 Dec 20238 Dec 2023

Publication series

Name2023 IEEE Globecom Workshops, GC Wkshps 2023

Conference

Conference2023 IEEE Globecom Workshops, GC Wkshps 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period4/12/238/12/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • digital twin
  • internet of things
  • internet of vehicles
  • reinforcement learning

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