Distributed TDMA for Mobile UWB Network Localization
Bibliographic record
Abstract
Many applications related to the Internet of Things, such as tracking people or objects, robotics, and monitoring require the localization of large networks of devices in dynamic, GPS-denied environments. Ultrawideband (UWB) technology is a common choice because of its precise ranging capability. However, allowing access and effective use of the shared UWB medium with a constantly changing set of devices faces some particular challenges: high frequency of ranging measurements by the devices to improve system accuracy; network topology changes requiring rapid adaptation; and decentralized operation to avoid single points of failure. In this article, we propose a novel time-division multiple access (TDMA) algorithm that can quickly schedule the use of the UWB medium by a large network of devices without collisions in local network neighborhoods and avoiding conflicts with hidden terminals, all the while maximizing network usage. Using exclusively the UWB radio network, we realize a decentralized system for synchronization, dynamic TDMA scheduling, and precise relative positioning on a multihop network. Our system does not have special nodes (all nodes are equal) and it is sufficiently scalable for real-world applications. Our method can be applied to implement device localization services in large spaces without GPS and complex topologies, such as malls, museums, mines, etc. We demonstrate our method in simulation and on real hardware in an underground parking lot, showing the effectiveness of its TDMA schedule for relative localization.
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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".