Electric Vehicle Cluster Assisted Multi-Tier Vehicular Edge Computing System: Cross-System Framework Design and Optimization
Bibliographic record
Abstract
Cooperative vehicle infrastructure is critical for facilitating multi-tier vehicular edge computing (VEC) systems to collaboratively process latency-sensitive intelligent tasks. However, the dynamic topology and the scarcity of available computing resources on real traffic roads pose significant challenges. While previous works have explored using public transit and parked vehicles as cooperative vehicles, they often overlook the incentive mechanisms and the specific tasks of these vehicles, rendering these solutions impractical. Motivated by the achievements in smart electric vehicles (EVs), this paper presents an electric vehicle cluster (EVC) assisted multi-tier VEC system that leverages the capabilities of the vehicle-to-grid (V2G) technology to provide stable computing resources to traffic areas. The proposed EVC-assisted multi-tier VEC system employs a cross-system two-level optimization method that jointly considers offloading and resource allocation decisions for road task vehicles (RTVs) in the VEC system and charging power decisions for the EVs in the EVC. At the lower level, we minimize the task latency for RTVs by utilizing the computing resources of idle EVs. At the upper level, we build a multi-agent two-step game model and introduce a potential game-based strategy and a Nash equilibrium (NE) based EV-RSU mapping method to derive the optimal decision strategy for the EVC. Extensive simulation results demonstrate the efficient reduction in total-task latency of RTVs and lower cost for the EVC while meeting the essential requirements in the V2G.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".