Energy-efficient transmission and bit allocation schemes in wireless sensor networks
Why this work is in the frame
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Bibliographic record
Abstract
Energy–efficient transmission and bit allocation schemes are investigated in multi–source single–sink Wireless Sensor Networks (WSNs). For transmission over Additive White Gaussian Noise (AWGN) channels with path loss, this work shows that the overall energy consumption can be minimised if each sensor transmits with the minimum power and cooperates with others in Time–Division Multiple Access (TDMA) mode. From the efficient correlated source coding perspective, the Slepian–Wolf coding theorem is applied. Jointly considering the two aspects, we propose a closed form bit allocation scheme to minimise the overall energy consumption. The underlying idea is to assign more bits to nodes with better channel conditions. Additionally, based on the definition of network lifetime as the time before the first sensor fails, we further maximise the network lifetime by developing a heuristic algorithm to balance energy consumption among sensors. The superiority of the proposed scheme is validated by both analytical and simulation results.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it