With the continued growth of its core technologies, including the Internet of
Things (IoT), artificial intelligence (AI), Big Data and data analytics, E
edge computing, digital twin (DT) technology has witnessed a significant
increase in industrial applications, helping the industry become more
sustainable, smart, and adaptable. Hence, DT technology has emerged as a
promising link between the physical and virtual worlds, enabling simulation,
prediction, and real-time performance optimization. This work aims to explore
the development of a high-fidelity digital twin framework, focusing on
synchronization and accuracy between physical and digital systems to enhance
data-driven decision making. To achieve this, we deploy several stationary UAVs
in optimized locations to collect data from industrial IoT devices, which were
used to monitor multiple physical entities and perform computations to evaluate
their status. We consider a practical setup in which multiple IoT devices may
monitor a single physical entity, and as a result, the measurements are
combined and processed together to determine the status of the physical entity.
The resulting status updates are subsequently uploaded from the UAVs to the
base station, where the DT resides. In this work, we consider a novel metric
based on the Age of Information (AoI), coined as the Age of Digital Twin
(AoDT), to reflect the status freshness of the digital twin. Factoring AoDT in
the problem formulation ensures that the DT reliably mirrors the physical
system with high accuracy and synchronization. We formulate a mixed-integer
non-convex program to maximize the total amount of data collected from all IoT
devices while ensuring a constrained AoDT. Using successive convex
approximations, we solve the problem, conduct extensive simulations and compare
the results with baseline approaches to demonstrate the effectiveness of the
proposed solution.

Questo articolo esplora i giri e le loro implicazioni.

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