Opportunistic WiFi offloading in vehicular environment: A queueing analysis
Bibliographic record
Abstract
In this paper, we present an analytical framework for offloading cellular traffic by outdoor WiFi network in the vehicular environment. Specifically, we consider a generic vehicular user with Poisson data service arrivals to download/upload data from/to the Internet through the cost-effective WiFi network (want-to) or the cellular network providing full service coverage (have-to). Under this scenario, the WiFi offloading performance, characterized by offloading effectiveness, is analyzed in terms of desired average service delay which is the average time the data services can be deferred for WiFi availability. We establish an explicit relation between offloading effectiveness and average service delay by an M/G/l/K queueing model, and the tradeoff between the two is examined. We validate our analytical framework through simulations based on a VANET simulation tool VANETMobisim and real map data sets. Our analytical framework should be valuable for providing offloading guidelines to both vehicular users and network operators.
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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 | 0.005 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 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".