Opportunistic WiFi offloading in vehicular environment: A queueing analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it