Scheduling Policy for Value-of-Information (VoI) in Trajectory Estimation for Digital Twins

TL;DR

This paper introduces a VoI-based scheduling algorithm for sensor data collection in digital twins, optimizing resource use and improving state estimation accuracy under network constraints.

Contribution

It proposes a polynomial-time VoI-based method for selecting the most informative sensors, enhancing digital twin state estimation efficiency.

Findings

Reduces communication overhead by 50%

Maintains estimation accuracy with fewer sensors

Outperforms benchmark scheduling methods

Abstract

This paper presents an approach to schedule observations from different sensors in an environment to ensure their timely delivery and build a digital twin (DT) model of the system dynamics. At the cloud platform, DT models estimate and predict the system's state, then compute the optimal scheduling policy and resource allocation strategy to be executed in the physical world. However, given limited network resources, partial state vector information, and measurement errors at the distributed sensing agents, the acquisition of data (i.e., observations) for efficient state estimation of system dynamics is a non-trivial problem. We propose a Value of Information (VoI)-based algorithm that provides a polynomial-time solution for selecting the most informative subset of sensing agents to improve confidence in the state estimation of DT models. Numerical results confirm that the proposed method outperforms other benchmarks, reducing the communication overhead by half while maintaining the required estimation accuracy.