Getting the most of WiFi mesh networks with 802.16 mesh emulation
Bibliographic record
Abstract
Currently deployed wireless mesh networks are based on 802.11, WiFi technology, which is not efficient in multihop scenarios. We present a method, which emulates 802.16 mesh networks over 802.11 hardware. The method works by embedding 802.16 packets into 802.11 broadcast packets and padding the 802.11 broadcast payload, so that the broadcasts are aligned to 802.16 time division multiple access frame boundaries. The method requires only software changes on the nodes using 802.11a for mesh communications. This means that the mesh networks installed with 802.11a hardware today can be upgraded with a software patch to take advantage of quality-of-service available in 802.16. We use ns2 simulations to show the performance of the 802.11 based mesh networks with the embedded 802.16. We show that the hybrid system can achieve throughputs in multiples of what is possible with 802.11 hardware alone. First, the efficiency of the new system is significantly higher than the efficiency of 802.11 based systems, because we use broadcast packets. Second, the new system eliminates unnecessary collisions in the wireless channel since it takes advantage of scheduled wireless access with 802.16 mesh coordination function.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".