Experiments of multi-channel 802.11 wireless mesh networks with TCP proxies
Bibliographic record
Abstract
IEEE 802.11 wireless access technology is a possible candidate for constructing wireless mesh networks. However, multi-hop 802.11 wireless networks suffer heavy co-channel interference. In this paper, the 802.11-based networks are extended to operate using multi-radio multi-channel designs to inhibit the interference effects. Using a partially overlapped channel scenario and an orthogonal channel scenario, it has been confirmed that the introduction of multiple channels is capable of improving network performance. Despite these gains, TCP performance degrades exponentially with hop counts; therefore, wireless mesh networks may further be improved by adding an n-hop proxy service. In terms of hop counts, these proxies break long connections into relatively shorter connections with tighter transport layer control. A trade-off between the number of proxies and the length of proxies has become evident through testbed evaluation. With respect to this trade-off, the queuing delays at proxies and the amount of collisions over the lossy wireless links signify the need for a suitable protocol to control the efficient usage of multiple channels and proxies.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".