Localization scheduling in wireless ad hoc networks
Bibliographic record
Abstract
Localization systems typically make use of redundant range or angle measurements from as many neighbours as possible to reduce location error. In dense networks this comes at the cost of excessive messages with limited improvement in location accuracy. This paper presents a scheduling scheme to limit node density to a set of active reference devices, which reduces message overhead and increases network lifetimes while maintaining sufficiently accurate location estimates. We use a method to estimate the amount of location error within a given probability to determine the minimum number of reference devices required for accurate localization. Simulation results show that with the proposed scheme localization messages remain constant with increasing node density, while location error is only marginally affected. Furthermore, network lifetimes are increased by over ten times allowing localization for longer periods of time. Computation times for localization are also reduced, which decreases accumulation of location error due to computation latency for mobile devices with speeds under 20 m/s.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".