Dynamic MEC resource management for URLLC in Industry X.0 scenarios: a quantitative approach based on digital twin networks

Abstract

The use of innovative technologies in Industry X.0 sce narios, 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 per formances 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 quan titatively 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.