FW-DAS: Fast Wireless Data Access Scheme in Mobile Networks
Bibliographic record
Abstract
In wireless data access applications, reduction of both the access latency and the wireless traffic volume is essential. In this paper, we propose a fast wireless data access scheme (FW-DAS) for wireless data access applications in which data objects are frequently updated and fast access to data objects is indispensable. In FW-DAS, different operation modes are defined depending on the data object popularity, and only popular data objects are proactively pushed to the access point/base station to minimize the access latency while mitigating the traffic load over the wireless link. An analytical model for the access latency is developed and an operation mode selection algorithm is introduced to reduce the access latency. Extensive simulation results show the effects of access-to-update ratio, data popularity, cache size, data object size, and wireless bandwidth. Analytical and simulation results demonstrate that FW-DAS can reduce the access latency with reasonable traffic load compared with poll-each-read (PER)/callback (CB) and their combinations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".