Bibliographic record
Abstract
Wireless mesh networks based on 802.11 technology could potentially be an inexpensive means of constructing large-scale wireless infrastructure networks. Wireless mesh networks attempt to capitalize on multiple hop communication to achieve transmissions over relatively larger distances. One fundamental concern is that multi-hop wireless networks may suffer heavily from co-channel interference. If multiple channels from the 802.11 spectrum are employed across adjacent links of communication, the interference effects can be mitigated. In practice, either overlapping channels or independent orthogonal channels can be assigned to the different links with varying effects. Topology control can be used to help manage these channels to limit the interference effects while providing for the necessary capacity and scalability requirements. By means of analyses and testbed experiments, I have validated that the introduction of multiple channels can improve overall system performance. With respect to the end-users, end-to-end performance over multiple wireless hops should be the primary concern. Under UDP-based communication sessions, network congestion is not the main contributor to transport layer performance degradation. Upon further investigation, TCP performance degrades exponentially with hop count, because it incorrectly interprets lost packets as a sigh of network congestion. Since TCP performance weakens for connections with more wireless hops, I further evaluate if network performance can be improved by adding an n-hop TCP proxy service. These proxies have the effect of breaking long connections into shorter connections with tighter transport layer control. A trade-off between the number of proxies and the hop count between proxies becomes evident through testbed evaluation. Analyzing various mesh characteristics and the relationships between MAC and transport layers can help establish a suitable protocol for future work.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".