Efficient aggregation using first hop selection in WSNs
Why this work is in the frame
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Bibliographic record
Abstract
We study the approaches to extending the lifetime of Wireless Sensor Networks (WSNs) based on in-network data aggregation at the First Hop (FH) away from each sensor data source, followed by flow-based routing of the resulting traffic. We introduce the concept of flow loss multiplier to express the impact of data aggregation on the conveyed data. A Mixed Integer Linear Programming (MILP) model is formulated for the problem of determining the optimal FH aggregation nodes that maximise network lifetime. Heuristics are proposed to obtain significant aggregation and to prolong the system lifetime. To facilitate performance evaluation, we adopt a flow loss multiplier that depends on the spatial relationship among sensed areas. Simulation results show that FH aggregation provides significant increase in network lifetime for both flow-based and tree-based delivery schemes, and that flow-based mechanisms provide better network lifetime than tree-based mechanisms but at the cost of added complexity and overhead.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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