Localised convex hulls to identify boundary nodes in sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intuitively, identification of nodes close to the network edge is key to the successful setup, and continued operation, of many sensor network protocols and applications. Many virtual coordinate constructions rely on the furthest set of nodes as beacons, and sensing applications may find useful the knowledge of the network edge. In this paper, we propose local convex view (lcv) as a means to identify nodes close to the network edge. It is motivated by the hypothesis that some structural information relevant to the network is buried within view of many nodes. The lcv differs from most previous methods in that it is a localised algorithm. Nodes using lcv may establish neighbourhood coordinates if no location information is available a priori. In those cases where needed information is missing, we adopt a simple probabilistic model to decide the boundary status of a node. We identify two metrics for evaluation and compare via simulation the performance of lcv against two methods with similar properties. Further simulations reveal the surprising observation that lcv seems unaffected by position estimation error. We enumerate and analyse a complete set of node configurations seen by lcv. We conclude that the geometric properties underlying lcv are responsible for its resilience to error.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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