Digital Twin-Native AI-Driven Service Architecture for Industrial
Networks
- URL: http://arxiv.org/abs/2311.14532v1
- Date: Fri, 24 Nov 2023 14:56:13 GMT
- Title: Digital Twin-Native AI-Driven Service Architecture for Industrial
Networks
- Authors: Kubra Duran, Matthew Broadbent, Gokhan Yurdakul, and Berk Canberk
- Abstract summary: 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.
- Score: 2.2924151077053407
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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