A Proxy Architecture to Enhance the Performance of WAP 2.0 by Data Compression
Bibliographic record
Abstract
This paper presents a novel proxy architecture for wireless application protocol (WAP) employing an advanced data compression scheme. Though optional in WAP , a proxy can isolate the wireless from the wired domain to prevent error propagations and to eliminate wireless session delays (WSD) by enabling long-lived connections between the proxy and wireless terminals. The proposed data compression scheme combines content compression together with robust header compression (ROHC), which minimizes the air-interface traffic data, thus significantly reduces the wireless access time. By using the content compression at the transport layer, it also enables TLS tunneling, which overcomes the end-to-end security problem in WAP 1.x. Performance evaluations show that while WAP 1.x is optimized for narrowband wireless channels, WAP utilizing TCP/IP outperforms WAP 1.x over wideband wireless channels even without compression. The proposed data compression scheme reduces the wireless access time of WAP by over in CDMA2000 1XRTT channels, and in low-speed IS-95 channels, substantially reduces access time to give comparable performance to WAP 1.x. The performance enhancement is mainly contributed by the reply content compression, with ROHC offering further enhancements.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".