The Permissioned Blockchain-Based Quantum-Inspired Edge Intelligence Approach for the Services of Future Internet of Vehicles
Bibliographic record
Abstract
The Internet of Vehicles (IoV) has become a key pillar in the future network system. However, intensive computing and task offloading required vehicles to compete for communication and computing resources, seriously affecting the systems time cost, robustness, and security. This paper focuses on solving resource management problems in the presence of interconnected multi-vehicles using shared information. We model this problem using a time-varying Markov decision process, addressing the challenges in task offloading for vehicles. The innovation lies in addressing different offloading scenarios, including vehicle-to-vehicle, vehicle-to-roadside unit (RSU) vehicle-to-multi-access edge computing (MAEC) server offloading, and vehicle-to-base station (BS). We propose a Quantum-inspired Dueling Deep Q-learning (QDDQL) algorithm to develop an Edge Intelligent (EI) offloading strategy. This method allows vehicles’ task offload to become an automated step based on network conditions and user status. The MAEC server offers computing offloading services, while the base station can submit offloading tasks to a cloud blockchain system. This innovative approach balances communication resource utilization, computational resource utilization, and transmission delay. Blockchain technology ensures transparency and security in resource allocation strategies, preventing edge nodes from making wrong decisions using consensus mechanism, and thereby improving the accuracy, timeliness, and security of resource allocation. Simulation results show that compared with existing methods, the proposed solution can significantly improve resource utilization, adaptability, and system scalability, and effectively address the defects of traditional methods.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".