On the Joint Placement of Blockchain and Users’ Virtualized Services in the Internet of Vehicles
Bibliographic record
Abstract
Driven by the evolution of the Internet of Things (IoT), Intelligent Transportation Systems are rapidly developing from traditional vehicle ad-hoc networks to the Internet of Vehicles (IoV). It supports various future applications, such as road traffic status detection and anti-collision warning for autonomous driving. IoV networks face several security and privacy challenges. Indeed, malicious nodes can compromise the system’s integrity and confidentiality by sending fake messages, thus jeopardizing human life. Blockchain has emerged as a potential solution to enhance IoV’s security given its unique features. To do so, this paper proposes a novel approach for joint resource allocation of blockchain and users’ virtualized services in IoV networks. Specifically, we formulate the joint placement problem of blockchain and user services’ virtualized network functions (VNFs) aiming to maximize the satisfaction of users’ service requests while realizing the shortest blockchain operation times related to those requests, as an integer linear program (ILP), known to be NP-hard. To solve it in a reasonable time, we propose two low-complexity solutions, the first is a meta-heuristic based on particle swarm optimization (PSO) and the second is a greedy solution. Through simulations, we evaluate the effectiveness of these approaches and their suitability for different service requirements.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".