Goal-Oriented Digital Twin for Operational Loss Minimization in 6G-Enabled Industrial Systems: A Joint Sensing and Control Approach
Bibliographic record
Abstract
Future 6G-enabled industrial systems will rely on distributed sensing and control over communication networks to manage concurrent processes, collaboratively achieving system-level operational objectives. However, various physical constraints, including excessive communication delays, complex interprocess dependencies, and dynamic system objectives, inevitably cause deteriorated operational outcomes compared to ideal conditions. To minimize this operational loss, we propose a goal-oriented digital twin (GDT) framework that overcomes these physical constraints through system orchestration in the virtual domain for dynamic objective fulfillment. Based on operational goals, the proposed GDT selectively integrates distributed sensing information into system digital twins, which then map system-level objectives into executable control tasks for individual devices. Specifically, by continuously evaluating the goal relevance of sensing data from individual devices, distributed observations are selected and prioritized, enabling control-aware communication resource allocation that balances control performance and communication efficiency. Moreover, delay-compensated control commands are accurately derived within the GDT framework, where the sensed temporal synchrony and interprocess dependencies are intentionally considered for coordinated task execution across distributed devices. Through this cohesive joint sensing and control design in the virtual domain, system-level objectives are fulfilled with minimized operational loss. Extensive simulations validate that GDT significantly improves control accuracy and resource efficiency in large-scale industrial systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".