RT info:eu-repo/semantics/conferenceObject
T1 Dynamic MEC resource management for URLLC in Industry X.0 scenarios: a quantitative approach based on digital twin networks
A1 Becattini, Marco
A1 Carnevali, Laura
A1 Fontani, Giovanni
A1 Paroli, Leonardo
A1 Scommegna, Leonardo
A1 Masoumi Estahbanati, Maryam
A1 Miguel Jiménez, Ignacio de
A1 Brasca, Fabrizio
K1 Industry X.0
K1 Ultra-Reliable and Low Latency  Communications (URLLC)
K1 Digital Twin Networks (DTN)
K1 Mobile Edge Computing (MEC)
K1 Stochastic modeling and analysis
AB The use of innovative technologies in Industry X.0 scenarios, including, but not limited to, Augmented Reality/Virtual Reality (AR/VR), autonomous robotics, and advanced security systems, requires applicative interconnections between a large number of IoT machines and devices. These interconnections must support Ultra-Reliable and Low Latency Communications (URLLC) to optimize usage and performances of devices related to those new technologies. Notably, the concepts of low latency and reliability are inherently linked; from a device perspective, any service exceeding specific response time thresholds is deemed unresponsive, and thus unreliable. In this paper, we present an innovative approach to quantitatively evaluate reliability in URLLC settings, leveraging the use of Digital Twin Networks (DTN), with a specific focus on Mobile Edge Computing (MEC) and its application to Industry X.0 scenarios. Results obtained so far show the potential for this approach to confer MEC better requests handling capabilities, by providing a near real time re-configuration ability within the MEC itself.
PB IEEE
SN 979-8-3503-8582-3
YR 2024
FD 2024
LK https://uvadoc.uva.es/handle/10324/76162
UL https://uvadoc.uva.es/handle/10324/76162
LA eng
NO 2024 IEEE International Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0 and IoT), Firenze, Italy, 2024, pp. 372-376
NO Producción Científica
DS UVaDOC
RD 19-jul-2025