Enhancing Data Dissemination Security and Quality Through the Authenticated Relay Selection and Scheduling Framework (ARSSF) in VANET
Bibliographic record
Abstract
A Vehicular Ad Hoc Network (VANET) is an emergent wireless technology that enables high-speed communication.It became attractive to automobile manufacturers because of the secure information transmission without fatal accidents.It has several unique features, such as data dissemination, frequently disconnected networks during transmission, dynamic network density, and dynamic topology, that differentiate VANETs.Data dissemination is vital because it ensures the safety and performance of the vehicle.Conversely, VANET has limitations in providing effective communication between the vehicles due to delays and frequent disruptions.Thus, data dissemination needs an effective routing and scheduling process to avoid collisions of vehicles.Based on this fact, we proposed a novel Authenticate Relay Selection and Scheduling Framework (ARSSF) for secure and quality data transmission in VANET.ARSSF is a novel VANET communication method that prioritizes trustworthy relay nodes and dynamic scheduling to increase network performance and safety as data dissemination needs rise.It used the SNR, utility, and relay selection schemes for choosing the relay nodes that can cover the capacity of the network, thus minimizing the data dissemination delay.Additionally, an authentication method was utilized during the relay transmission phase to ensure both data security and authorization.The proposed framework performance was assessed by NS2 simulation.The results demonstrate that ARSSF is beneficial.ARSSF's minimum delay of 47 ms, throughput of 98.17%, and security measures of 98.38% show its value in VANETs.It enhances VANET safety, efficiency, and data distribution and could serve as a high-end vehicular network research platform.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".