A Combined Push-Pull Access Framework for Digital Twin Alignment and Anomaly Reporting

2025-08-29

AI Summary

Digital twins (DTs) face a communication resource allocation trade-off between state synchronization and anomaly reporting: pull-based updates ensure alignment accuracy, whereas push-based updates guarantee timely anomaly response—yet both compete for limited bandwidth. To address this, we propose the Push-Pull Scheduler (PPS), the first framework to jointly orchestrate push and pull mechanisms under an Age of Information (AoII)-minimization objective. PPS dynamically allocates communication resources by jointly sensing real-time system states and event criticality, thereby enhancing DT alignment fidelity without compromising anomaly detection latency. Experimental results demonstrate that, under equivalent anomaly detection guarantees, PPS reduces AoII by over 20% and cuts worst-case anomaly detection delay from 70 ms to 20 ms.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Agent Communication
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

A digital twin (DT) contains a set of virtual models of real systems and processes that are synchronized to their physical counterparts. This enables experimentation and examination of counterfactuals, simulating the consequences of decisions in real time. However, the DT accuracy relies on timely updates that maintain alignment with the real system. We can distinguish between: (i) pull-updates, which follow a request from the DT to the sensors, to decrease its drift from the physical state; (ii) push-updates, which are sent directly by the sensors since they represent urgent information, such as anomalies. In this work, we devise a push-pull scheduler (PPS) medium access framework, which dynamically allocates the communication resources used for these two types of updates. Our scheme strikes a balance in the trade-off between DT alignment in normal conditions and anomaly reporting, optimizing resource usage and reducing the drift age of incorrect information (AoII) by over 20% with respect to state-of-the-art solutions, while maintaining the same anomaly detection guarantees, as well as reducing the worst-case anomaly detection AoII from 70 ms to 20 ms when considering a 1 ms average drift AoII constraint.

Problem

• Balancing digital twin alignment with anomaly reporting
• Optimizing communication resources for push-pull updates
• Reducing drift age of incorrect information in twins

Innovation

• Combined push-pull scheduler framework
• Dynamically allocates communication resources
• Reduces drift age by 20%