Blockchain-Based On-Demand Computing Resource Trading in IoV-Assisted Smart City
Bibliographic record
Abstract
In a smart city, Mobile Edge Computing (MEC) are generally deployed in static fashion in base stations (BSs). While moving vehicles with advanced on-board equipment can be regarded as dynamic computing resource transporters ignoring geographical limitations. Thus Internet of Vehicle (IoV) could assist the smart city to achieve flexible computing resource demand response (DR) via paid sharing the idle vehicle computing resources. Motivated by this, we propose a Peer-to-Peer (P2P) computing resource trading system to balance computing resource spatio-temporal dynamic demands in IoV-assisted smart city. On one hand, to guarantee transaction security and privacy-preserving in our system, we employ a consortium blockchain approach and demonstrate the process of secure computing resource trading without involving a centralized trusted third-party. On the other hand, to encourage individual smart vehicles to participate in our system, we construct a two-stage Stackelberg game jointly optimizing the utilities of buyers and sellers. And we also derive the optimal computing pricing and trading amount strategies in this proposed game. Finally, security analysis shows the security performance of our system and numerical simulations show that our strategies can encourage the collaboration between the buyer and smart vehicles.
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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) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".