Ph. D. Project
Title:
Analyzing the Impact of Wireless Network Parameters on the Accuracy of Digital Twin Models Using Artificial Intelligence
Dates:
2025/09/15 - 2028/09/14
Student:
Supervisor(s):
Description:
The convergence of wireless networks and digital twin technology has transformed the ability to monitor, analyze, and optimize complex physical systems in real time. Digital twins,which create virtual representations of physical systems, rely on accurate data transmission from wireless networks to function effectively. The accuracy of these digital twins is directly influenced by the
quality and reliability of the data they receive, which depends on the performance of the wireless network connecting IoT devices to the digital twin environment.
In wireless networks, parameters such as network topology, signal interference, data rates, latency, mobility, energy consumption, and security protocols have a significant impact on data transmission. These parameters not only affect the speed and reliability of the network but also influence the precision of the data being relayed to digital twins. By investigating the relationship
between these wireless network characteristics and the accuracy of digital twin models, this thesis aims to provide a comprehensive understanding of how network configurations affect the accuracy of virtual representations.
This thesis aims to investigate how various wireless network parameters, including topology, interference, data rates, andmobility, impact the accuracy of digital twins. The study will explore the application of AI techniques, such as machine learning, predictive modeling, and optimization algorithms, to dynamically optimize network performance, predict network behavior, and improve the accuracy of digital twins.
Bibliography:
[1] Khan, L. U., Han, Z., Saad, W., Hossain, E., Guizani, M., & Hong, C. S. (2022). Digital twin of wireless systems: Overview, taxonomy, challenges, and opportunities. IEEE Communications Surveys & Tutorials, 24(4), 2230-2254.
[2] Zeb, S., Mahmood, A., Hassan, S. A., Piran, M. J., Gidlund, M., & Guizani, M. (2022). Industrial digital twins at the nexus ofNextGwireless networks and computational intelligence: A survey. Journal ofNetwork and Computer Applications, 200, 103309.
[3] Maimour, M., Ahmed, A., & Rondeau, E. (2024). Survey on digital twins for natural environments: A communication network perspective. Internet of Things, 101070.
[4] Apostolakis, N., Chatzieleftheriou, L. E., Bega, D., Gramaglia, M., & Banchs, A. (2023). Digital twins for next-generation mobile networks: Applications and solutions. IEEE Communications Magazine, 61(11), 80-86.
quality and reliability of the data they receive, which depends on the performance of the wireless network connecting IoT devices to the digital twin environment.
In wireless networks, parameters such as network topology, signal interference, data rates, latency, mobility, energy consumption, and security protocols have a significant impact on data transmission. These parameters not only affect the speed and reliability of the network but also influence the precision of the data being relayed to digital twins. By investigating the relationship
between these wireless network characteristics and the accuracy of digital twin models, this thesis aims to provide a comprehensive understanding of how network configurations affect the accuracy of virtual representations.
This thesis aims to investigate how various wireless network parameters, including topology, interference, data rates, andmobility, impact the accuracy of digital twins. The study will explore the application of AI techniques, such as machine learning, predictive modeling, and optimization algorithms, to dynamically optimize network performance, predict network behavior, and improve the accuracy of digital twins.
Bibliography:
[1] Khan, L. U., Han, Z., Saad, W., Hossain, E., Guizani, M., & Hong, C. S. (2022). Digital twin of wireless systems: Overview, taxonomy, challenges, and opportunities. IEEE Communications Surveys & Tutorials, 24(4), 2230-2254.
[2] Zeb, S., Mahmood, A., Hassan, S. A., Piran, M. J., Gidlund, M., & Guizani, M. (2022). Industrial digital twins at the nexus ofNextGwireless networks and computational intelligence: A survey. Journal ofNetwork and Computer Applications, 200, 103309.
[3] Maimour, M., Ahmed, A., & Rondeau, E. (2024). Survey on digital twins for natural environments: A communication network perspective. Internet of Things, 101070.
[4] Apostolakis, N., Chatzieleftheriou, L. E., Bega, D., Gramaglia, M., & Banchs, A. (2023). Digital twins for next-generation mobile networks: Applications and solutions. IEEE Communications Magazine, 61(11), 80-86.
Keywords:
Wireless networks performance, digital twin accuracy, AI, optimization
Department(s):
| Modelling and Control of Industrial Systems |