On Developing Smart Transportation Applications in Fog Computing Paradigm
Bibliographic record
Abstract
Smart Transportation applications by nature are examples of Vehicular Ad-hoc Network (VANETs) applications where mobile vehicles, roadside units and transportation infrastructure interplay with one another to provide value added services. While there are abundant researches that focused on the communication aspect of such Mobile Ad-hoc Networks, there are few research bodies that target the development of VANET applications. Among the popular VANET applications, a dominant direction is to leverage Cloud infrastructure to execute and deliver applications and services. Recent studies showed that Cloud Computing is not sufficient for many VANET applications due to the mobility of vehicles and the latency sensitive requirements they impose. To this end, Fog Computing has been proposed to leverage computation infrastructure that is closer to the network edge to compliment Cloud Computing in providing latency-sensitive applications and services. However, applications development in Fog environment is much more challenging than in the Cloud due to the distributed nature of Fog systems. In this paper, we investigate how Smart Transportation applications are developed following Fog Computing approach, their challenges and possible mitigation from the state of the arts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".