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

This letter 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 sched...

Full description

Saved in:
Bibliographic Details
Published in:IEEE communications letters 2023-06, Vol.27 (6), p.1654-1658
Main Authors: Bui, Van-Phuc, Pandey, Shashi Raj, Chiariotti, Federico, Popovski, Petar
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Computational modeling
Digital twin
Digital twins
dynamic systems
Internet of Things
Polynomials
Predictive models
Reliability
Resource allocation
Resource scheduling
scheduling policies
sensor networks
Sensors
State estimation
State vectors
System dynamics
Time division multiple access
Trajectory analysis
Wireless sensor networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This letter 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.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2023.3264945