A Fog-based Reputation Evaluation Model for VANETs
Bibliographic record
Abstract
Fog computing can play an important role in Vehicular Ad Hoc Networks (VANETs) in enhancing the quality of fog-based services. The idea of partial reliance on fog computing to support the existing infrastructure has been explored in few research papers. Fog computing holds the promise of significant potential benefits to edge users. The capabilities of fog and its position (i.e., the proximity from edge users) give fog the power to play a vital role in employing the most competent node. In other words, fog can reduce the workload that is required to do by the vehicles (e.g., propagating the event’s details, and evaluating the trust of the sender). In this paper, we deploy fog nodes to gather the trust evaluations from the vehicles, which allow fog nodes to rely on their local vehicles to do certain tasks. Also, fog nodes are used in this work to keep the records of its local vehicles to reduce the need to communicate with the cloud. Also, we proposed a scheme using Task-based Experience Reputation (TER), which reflects the vehicle’s reputation in performing certain tasks. Finally, we shed the light on the issue of two commonly used trust updating methods and, we proposed applying the concept of TER to solve this issue. The proposed model reduces the message transmission overhead and workload on the vehicles compared to experience-based trust models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".