Double-Auction-Based Task Offloading in VEC via Multi-Agent Reinforcement Learning
Bibliographic record
Abstract
Task offloading in Vehicular Edge Computing (VEC) can significantly enhance Cooperative Perception (CP) for Autonomous Vehicles (AVs), improving situational awareness and traffic safety. However, the widespread adoption of VEC is often constrained by the high deployment costs of Roadside Units (RSUs). In this paper, we propose the Truthful and Quality-Aware Task Offloading (TQTO) scheme. TQTO leverages the prolific yet underutilized computational resources of parked vehicles for CP tasks in VEC to alleviate RSU scarcity. Using vehicle-to-vehicle (V2V) communication, parked vehicles can be strategically involved in CP processing and are incentivized to contribute their resources. TQTO introduces a distributed, truthful, double-auction-based multi-agent deep reinforcement learning framework that enables user vehicles to offload CP tasks to parked vehicles in a utility-maximizing manner, while respecting their individual budget constraints. Concurrently, TQTO considers the provider-side (i.e., parked vehicles) costs and ensures a truthful, incentive-compatible, and budget-balanced marketplace for VEC. A critical value-based payment mechanism is used to ensure fair compensation for parked vehicles and to align task requesters’ payments with their utility. TQTO formulates the task offloading problem as a Double Auction Quadratic Multiple Knapsack Problem (DA-QMKP) and solves it using a QMIX-based heuristic for scalable decision-making under partial observability. Extensive evaluations show that TQTO outperforms a prominent non-auction-based scheme by up to 39% in terms of social welfare.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".