A Distributed and Dynamic Data Gathering Protocol for Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we propose a distributed, self organizing, robust and energy efficient data gathering algorithm for sensor networks operating in environments where all the sensor nodes are not in direct communication range of each other and data aggregation is used while routing. Proposed algorithm is based on local minimum spanning tree (LMST) structure, which nodes can construct from the position of their 1-hop neighbors. Reporting tree is constructed from the sink by allowing only edges of LMST to join the tree, plus possibly some direct links to the sink. Each node selects as parent the LMST neighbor so that the total energy cost of route to the sink is minimal. We also describe route maintenance protocols to respond to predicted sensor failures and addition of new sensors. Our simulation results show that our algorithm prolongs the network lifetime significantly compared to some alternative 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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