Reliability-Aware Scheduling for Digital Twin Maintenance

2026-08-22

AI Summary

本文研究了在工业物联网系统中，通过使用Ensemble Disagreement Indicator和R-VoU调度器优化数字孪生的维护更新问题，以减少通信限制下的模型误差。

Abstract

In Industrial Internet of Things systems, learning-enabled Digital Twins (DTs) support remote monitoring by using data reported by distributed devices to maintain digital representations of physical processes. When uplink resources are limited, a base station cannot collect new observations from every device at every communication slot and must decide which devices should transmit. This decision becomes challenging when the physical process changes after the DT models have already been trained. In such cases, recent observations alone may not keep the DT accurate, because the learned model may no longer match the underlying process. This paper studies how to schedule observation requests so that the DT maintained at the base station remains close to the true physical process. We define the Ensemble Disagreement Indicator (EDI) as an uncertainty measure computed at the base station from the spread among estimates produced by independently trained DT predictors. Building on EDI, we propose R-VoU, a reliability-aware value-of-update scheduler that prioritizes the observations expected to most improve the DT by reducing a cost based on uncertainty and predicted DT error. The DT is further adjusted online using the difference between received observations and current predictions. Experiments on process manufacturing data show that, under limited communication budgets, R-VoU achieves the lowest combined cost and DT estimation error among the compared schedulers that use only information available before each scheduling decision.

Problem

• Digital Twins
• Scheduling
• Reliability
• Industrial Internet of Things
• Uplink Resources

Innovation

• Ensemble Disagreement Indicator (EDI)
• Reliability-aware Scheduling
• Value-of-Update (VoU)