Network Navigation With Scheduling: Distributed Algorithms
Why this work is in the frame
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Bibliographic record
Abstract
Network navigation is a promising paradigm for enabling location awareness in dynamic wireless networks. A wireless navigation network consists of agents (mobile with unknown locations) and anchors (possibly mobile with known locations). Agents can estimate their locations based on inter-and intra- node measurements as well as prior knowledge. With limited wireless resources, the key to achieve high navigation accuracy is to maximize the benefits of agents' channel usage. Therefore, it is critical to design scheduling algorithms that adaptively determine with whom and when an agent should perform inter-node measurements to achieve both high navigation accuracy and efficient channel usage. This paper develops a framework for the design of distributed scheduling algorithms in asynchronous wireless navigation networks, under which the algorithm parameters are optimized based on the evolution of agents' localization errors. Results show that the proposed algorithms lead to high-accuracy, efficient, and flexible network navigation.
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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 | 0.000 | 0.001 |
| 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 it