Cooperative Computation Offloading in Blockchain-Based Vehicular Edge Computing Networks
Bibliographic record
Abstract
As a novel computing paradigm, multiaccess edge computing (MEC) migrates computing and storage capabilities to edge nodes of the network to meet the requirements of executing computationally intensive or delay-sensitive tasks on intelligent vehicles. In addition, MEC fills the gap between cloud computing and terminals in vehicular networks. In the MEC system, to reduce the load on MEC servers with large-scale vehicle deployment and promote the efficient use of network resources, vehicles can also transfer tasks to neighboring resource-rich vehicles using cooperative computation offloading. However, cooperative computation offloading between vehicles faces the challenges of security and insufficient information about the server vehicle. Therefore, this paper proposes using blockchain technology to achieve efficient data sharing between vehicles and service providers (i.e., server vehicles) and ensure the security of computation offloading between vehicles. First, we design a secure data sharing architecture in blockchain-based vehicular edge computing networks. Then, a new consensus mechanism in this architecture is proposed to improve the efficiency of data sharing and prevent malicious attacks. Furthermore, we present a cooperative offloading decision-making method using an offloading game, and the Nash equilibrium of the offloading strategy is achieved using this method. The results of numerical experiments demonstrate the superior performance of the proposed method.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".