Dynamic Task Scheduling for Digital Twins to Meet Telesurgery Real-Time Requirements
Bibliographic record
Abstract
Recent developments in 6G technology are projected to revolutionize applications with stringent real-time requirements, such as telesurgery. This paper presents a novel approach for supporting real-time decision-making in remote surgery by leveraging digital twins to overcome distance barriers. We address the communication challenges inherent to telesurgical digital twins and introduce an end-to-end formulation of data synchronization using Integer Linear Programming (ILP). We propose various grouping strategies for critical surgical tasks to support dynamic task scheduling within the telesurgical digital twin environment. These strategies include Interrelated Dependency (IRD), Highest Priority-Nearest Deadline (HPND), Shortest Transmission Time (STT), and Shortest Validity Time (SVT). We apply the knapsack approach to optimize task scheduling, aiming to maximize the sum of task priorities while minimizing the number of tasks that miss their deadlines. Performance evaluation shows that the HPND grouping strategy yields better results than other grouping strategies across key performance metrics, including transmitted task size, task priorities, and the reduction of tasks that miss their deadlines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".