Analyzing the Impact of Wireless Network Parameters on the Accuracy of Digital Twin Models Using Artificial Intelligence

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 network parameters, including topology, interference, data rates, and mobility, 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.

The Research Center for Automatic Control (CRAN) is a joint research unit between the University of Lorraine and the French National Scientific Research Center (CNRS) - Institute for Information Sciences and Technologies (INS2I).

Based on digital sciences, the laboratory is internationally recognized for its activities in the fields of signal and image processing, control and computer engineering, as well as for its work in health in connection with biology and neuroscience. Today, its fundamental and applied research enables it to accompany the changes in society and goes beyond the traditional industrial issues: energy production, management of the intelligent city or transport. In health, it is opening up to diagnosis and care in cancerology and neurology. They are crossing sociology, listening to social behaviors and opinion dynamics, and investing in the field of sustainable development, in the service of the circular economy and ecological systems.

- Master / Engineering degree in Computer Science or a related field

- Knowledge in Networks

- Skills in Simulation tools and development