Age of Information in Digital Twin Migration
Bibliographic record
Abstract
A Digital Twin (DT) is a virtual representation of a real physical system (PS) that interacts with other objects on its behalf. In these interactions, the Age of Information (AoI) is a key performance measure that is dependent on the DT's current network server placement. To maintain acceptable AoI performance as the system evolves, the DT location may have to be moved, which is referred to asDigital Twin migration. In this paper we consider the problem of DT migration in a vehicular system, focusing on minimizing the time-averaged AoI. In this type of system, it is difficult to maintain acceptable AoI performance due to the speed of the vehicles, which can result in frequent abrupt handoffs between different cellular domains. This makes the question of when to initiate DT migration an important one. The problem is formulated as a Markovian stopping problem and an optimal online algorithm is proposed using dynamic programming and the statistics of vehicular motion. A more computationally intensive adaptive version of this algorithm is also proposed where the dynamic programming tables are recomputed at each time step. A best-in-expectation algorithm is introduced that gives sub-optimal AoI performance but is more computationally efficient than in the optimal version. These algorithms are also compared to heuristics that do immediate migration and migration at handoff. An offline algorithm is formulated that provides a lower bound on the average AoI that is attainable. Performance results show that the proposed algorithm can significantly improve the efficiency of Digital Twin migrations compared to the other approaches while guaranteeing the minimized time-averaged AoI.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".