An AODV-improved routing based on power control in WiFi mesh networks
Bibliographic record
Abstract
Most existing ad hoc routing protocols attempt to minimize the number of hops from source to destination pairs. These routing protocols are all designed under the assumption of using only single data rate in the wireless channel. One of the current trends in wireless communication (e.g., IEEE 802.11b) is to enable devices to operate using many different transmission rates in order to accommodate a wide range of channel conditions. This paper introduces a routing algorithm utilizing the multi-rate and multi-range capacity in WiFi (wireless fidelity) mesh networks. Since transmission rate is inversely proportional to transmission range and every range corresponds with a transmit power level, we propose selecting high data-rate route by adjusting the transmit power level when establishing the route. The characteristic of this method is power control, it discovers the required data-rate link within the transmission range through adjusting the transmit power to corresponding level. We show through simulation that the proposed technique improves the network throughput and minimizes the power consumption due to its utilization of multi-rate support from MAC and physical layers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".