  The paper examines a scenario wherein sensors are deployed within an
Industrial Networked Control System, aiming to construct a digital twin (DT)
model for a remotely operated Autonomous Guided Vehicle (AGV). The DT model,
situated on a cloud platform, estimates and predicts the system's state,
subsequently formulating the optimal scheduling strategy for execution in the
physical world. However, acquiring data crucial for efficient state estimation
and control computation poses a significant challenge, primarily due to
constraints such as limited network resources, partial observation, and the
necessity to maintain a certain confidence level for DT estimation. We propose
an algorithm based on Value of Information (VoI), seamlessly integrated with
the Extended Kalman Filter to deliver a polynomial-time solution, selecting the
most informative subset of sensing agents for data. Additionally, we put forth
an alternative solution leveraging a Graph Neural Network to precisely
ascertain the AGV's position with a remarkable accuracy of up to 5 cm. Our
experimental validation in an industrial robotic laboratory environment yields
promising results, underscoring the potential of high-accuracy DT models in
practice.
