Digital Twin Placement in Vehicular Networks Using Dynamic Flow Network Evacuation
Bibliographic record
Abstract
A digital twin (DT) is a software version of a physical system (PS) that interacts with other objects on its behalf. In order to do so, changes in the PS must be communicated to the DT in a timely fashion, and this updating is referred to as DT synchronization. This paper addresses the Minimum Synchronization Period (MSP) problem in vehicular networks, which seeks to place DTs on execution servers (ESs) so as to minimize the maximum synchronization period for all physical systems and their DTs (PS-DT pairs), while satisfying communication and computation requirements. A novel solution is proposed by modelling the MSP problem as a multi-commodity quickest flow evacuation problem, which treats the synchronization data and processing as flow network inputs to be evacuated in the shortest possible time. Transmission and computation components are represented as network flows with linear edge delays, which enables the use of well-known techniques to find the quickest flow solution. To ensure that each DT is placed at a single execution server, an unsplittable flow rounding procedure is used that assigns DTs to servers without significantly increasing the synchronization objective. Simulation results demonstrate the quality of the MSP solutions produced by our algorithm using the optimal fractional solution as a lower bound for the optimal integral solution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".