Position Estimation Error in Edge Detection for Wireless Sensor Networks using Local Convex View
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. In a previous study [1] we introduced local convex view (lcv) as a means to identify nodes close to the network edge by computing the convex hull of nodes within range. In this paper we evaluate lcv in the presence of position estimation error. Extensive simulations with networks of varying size and topology reveal the surprising observation that lev seems unaffected by estimation error. Motivated by this observation we enumerate a complete set of base node configurations seen by lcv. An analysis reveals that lcv is immune to two of these configurations. Further simulations show the frequency of false-positives and false-negatives imposed by a third, ambiguous, configuration to be low. The frequency of the ambiguous case is 10% in the worst case, for all networks tested. We conclude that the geometric properties underlying lcv are responsible for its resilience to error.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".