Optimal Measurement Geometry Directed Integrated Localization and Synchronization in Large-Scale Wireless Networks
Bibliographic record
Abstract
Location awareness and time consensus, which are two intertwined aspects of distributed systems, have become more important in vertical industrial Internet of Things (IoT) applications. Existing integrated localization and synchronization (ILAS) in a connected system relies on collaborative measurement of time of arrival, as well as exchange of estimated location and clock related states. However, with the growing scale and dynamics of wireless IoT systems, the unselected and excessive information obtained from the collaborating nodes becomes less effective in ILAS. To enhance the performance of ILAS with controlled complexity, we first propose an optimal measurement geometry directed collaborating nodes selection scheme in this paper. Specifically, the optimal measurement geometry evaluated by the dilution of precision is utilized to prioritize the corresponding subset collaborating nodes for the best estimation accuracy with limited complexity. Moreover, to further reduce the computation complexity in increased-scale systems, a sequential state stacking belief propagation algorithm is proposed for the related states estimation, where the matrix inversions and square root calculations reduce to the dimensions of a subset of the overall collaborating states. Numerical simulations demonstrate a significant enhancement in the robustness of the ILAS estimation and reduction in the computational complexity compared to the baseline schemes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".