Joint Computation Offloading and Energy Trading in Electric Vehicular Networks
Bibliographic record
Abstract
With the rising number of electric vehicles (EVs), the high computational task and energy management of vehicles bring great challenges to the intelligent transportation system. In this work, we investigate the joint offloading and energy trading strategy in vehicular edge computing (VEC) network. We propose an offloading-trading framework, in which EVs can offload tasks to road side unit (RSU) equipped with edge servers or Energy Fog Center (EFC), i.e, edge nodes and fog nodes, and sell excess power to EFC through Vehicle-to-grid (V2G) technology to improve energy efficiency. We aim to maximize the system utility while satisfying the offloading-trading requirements. Since the original problem is non-convex, we decompose it into two subproblems, i.e., trading energy subproblem and trading-offloading subproblem, and proposed the Farthest and Nearest Comparison Searching (FNC-S) algorithm. Specifically, we derive the closed-form expressions of trading electric energy in the trading energy subproblem. Besides, trading-offloading strategy is obtained at two boundaries of distance based on optimal moving distance searching in the trading-offloading subproblem. Simulation results show that the proposed FNC-S algorithm can significantly improve the utility compared with other baseline schemes.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".