Energy-aware data-centric routing in microsensor networks
Why this work is in the frame
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Bibliographic record
Abstract
Large-scale wireless sensors are expected to play an increasingly important role in future civilian and military settings where collaborative microsensors could be very effective in monitoring their operations. However, low power and in-network data processing make data-centric routing in wireless sensor networks a challenging problem.In this paper, we propose a novel and efficient energy-aware distributed heuristic, which we refer to as EAD, to build a special rooted broadcast tree with many leaves that is used to facilitate data-centric routing in wireless microsensor networks. Our EAD algorithm makes no assumption on local network topology, and is based on residual power. It makes use of a neighboring broadcast scheduling and distributed competition among neighboring nodess. We discuss the implementation of our scheme, and present an extensive simulation experiments to study the its performance. Our experimental results indicate clearly that our EAD scheme outperforms previous schemes.
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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 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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