Scheduling in green vehicular infrastructure with multiple roadside units
Bibliographic record
Abstract
Smart scheduling can be used to reduce infrastructure energy costs in vehicular roadside networks [1]. In this paper we consider the scheduling problem when there are multiple roadside units (RSUs) in tandem. In this case it is often desirable to load balance the energy consumption across the roadside units so that energy provisioning costs can be reduced as much as possible. We first derive an integer linear programming bound on the min-max energy usage of the roadside units for a given input sample function. This bound is used for comparisons with two proposed on-line scheduling algorithms. The first is a low complexity First-Come-First-Assigned (FCFA) scheduler that makes greedy RSU selections followed by a minimum energy time slot assignment. The second algorithm, the Greedy Flow Graph Algorithm (GFGA), makes the same RSU selection but reassigns time slots whenever a new vehicle is assigned to the same RSU. This is done using a locally optimum integer linear program that can be efficiently solved using a minimum cost flow graph. Results from a variety of experiments show that the proposed scheduling algorithms perform well when compared to the energy lower bounds. Our results also show that near-optimal results are possible but come with increased computation times compared to our heuristic 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".