A Distributed TDMA Scheduling Algorithm for Target Tracking in Ultrasonic Sensor Networks
Bibliographic record
Abstract
Ultrasonic sensors are able to provide highly accurate measurements if they are properly scheduled, otherwise, the intersensor interference (ISI) could greatly deteriorate the performance. In addition, the scheduling scheme should be performed in a distributed and energy-efficient way so that it can be conveniently implemented for a large-scale network. In this paper, for target tracking with multiple ultrasonic sensors, we convert the ISI avoidance problem to the problem of multiple access in a shared channel and adopt the time division multiple access strategy which has the properties of being collision free and energy efficient. Then, by graph theory, the scheduling problem is transformed into a coloring problem which aims at minimizing the number of used colors. Since the original problem has been proved to be NP-hard, we propose a distributed-saturation-degree-based algorithm (DSDA) which can be implemented locally by each node with information collected from its neighbors. Furthermore, we verify that an interference-free schedule is guaranteed to be obtained by DSDA. We derive analytical results for the complexity of this algorithm. Specifically, for different sensor network topologies, we prove that the expected converging time and the expected message transmissions per node are both upper bounded byO(δ), where δ is the maximum neighborhood size in the network. Extensive simulations demonstrate the effectiveness of our algorithm.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".