Bibliographic record
Abstract
The design of Wireless Sensor Networks (WSNs) should focus on energy efficiency since wireless sensor nodes work on limited batteries, which are difficult or impossible to replace in most situations. However, the performance of WSNs is adversely impacted by fading effects, which requires much energy to combat for a high Bit Error Rate (BER) requirement. On the other hand, since the transmit energy is proportional to transmit distance, energy imbalance is caused by the different distances to the Base Station (BS). Sensor nodes far from the BS consume much more energy than those close to the BS and may die out quickly, which shortens the lifetime or results in a malfunction of the WSN. Reducing the proportion of transmit energy in total energy consumption helps minimize the differences in transmit energy among sensor nodes. Cooperative transmission has been proven to be an effective way to combat the impacts of fading by obtaining diversity gains and therefore, reduces the transmit energy. In this paper, we first apply cooperative transmission in WSNs, which not only reduces energy consumption but also lessens the differences of energy consumption among sensor nodes. To further balance energy among sensor nodes, we apply different cluster size and simulation results show that unequal cluster size improves energy balancing.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".