On Optimal Scheduling of OTA Software Updates for Smart Vehicles Leveraging Fog Computing
Bibliographic record
Abstract
Recently, the problem of optimal resource scheduling for the increasingly growing number of smart vehicles has emerged as a daunting research challenge. While Over the Air (OTA) software updates are critical for the safe routing/driving of smart vehicles, they consume precious network resources if not planned or scheduled appropriately. In this paper, we consider a smart fog computing network to serve the smart vehicles with OTA updates and present two optimal resource scheduling problems SP1and SP2. The objective of SP1is to minimize the maximum waiting time of any smart vehicle and the objective of SP2aims to minimize the maximum transmission time of any channel. These problems cannot always ascertain optimal solutions in polynomial time. We first propose a random algorithm and a greedy algorithm for these problems and find that these two algorithms may not perform adequately in many cases. Hence, we propose a local search algorithm which takes any feasible solution of a scheduling problem as input and improves that solution. Computer-based simulations demonstrate the effectiveness of our proposed algorithms.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".