SmartNode: achieving 802.11 MAC interoperability in power-efficient ad hoc networks with dynamic range adjustments
Bibliographic record
Abstract
The standard CSMA/CA based IEEE 802.11 protocol assumes that each node uses a certain fixed (or maximum) transmission power for the transmission of each packet. However, a MAC protocol with power adjustments can have significant benefits towards better power conservation and higher system throughput through better spatial reuse of spectrum. In this work, we propose a power efficient MAC layer algorithm, SmartNode, that is compatible with the basic RTS-CTS-DATA-ACK MAC protocol defined in IEEE 802.11. Our algorithm uses the minimum required power level for the transmission of data packets, which requires special handling for the exchange of control packets. Compared with previously proposed power-controlled MAC protocols, our algorithm does not require multiple data channels at the physical layer so that it is able to inter-operate with regular nodes running the existing IEEE 802.11 MAC protocol. Through extensive performance evaluations, we have demonstrated that our proposed algorithm is effective in a power-controlled ad hoc network - it is able to increase system throughput while conserving power with dynamic power adjustments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".