Efficient Task Offloading in NOMA-Assisted Vehicular Fog Computing for Customer Applications
Bibliographic record
Abstract
Future Fog computing networks are the basis of many smart customer applications in the areas of transportation and healthcare. Timely execution of application related tasks is a key challenge in such fog computing networks so that application reliability and customer satisfaction can be maximized. In this paper, we focus on vehicular fog computing scenario and utilize Non-Orthogonal Multiple Access (NOMA) to improve the spectral efficiency of task transmission from vehicles to fog nodes. To tackle the NOMA pairing problem, we propose a Hungarian-based algorithm to minimize task computational delay. We consider multiple factors such as task priority, vehicle to fog node transmission rate, and current load at the fog nodes to optimize the pairing of vehicles for task transmission. For multi-criteria decision making, we utilize Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate the ranking scores that are used in the Hungarian algorithm. Realistic simulation results for the proposed technique shows improved task computational delay for high priority tasks as compared to other techniques in the literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".