On TDMA scheduling in wireless sensor networks
Bibliographic record
Abstract
We study the problem of finding an efficient schedule for convergecast in a wireless sensor network, using the SINR model of interference, and a TDMA protocol for medium access. We compare two approaches to computing a minimum length schedule for the TDMA frame. In the first approach, called the TC-approach, a multi-set of transmission configurations that are interference-free and that cover the convergecast traffic is computed and the scheduling algorithm restricts itself to using these configurations. In the second approach, called the tree-based approach, first a routing tree or subgraph is computed, and next, sets of non-interfering links are scheduled in rounds, based on which links have available traffic in each round. In this paper, For the TC-based approach, we provide new column generation approach and several new scheduling heuristics. For the tree-based approach, we propose the construction of a new tree called TFM tree, which takes into account variable power assignment, as well as a new scheduling algorithm for the second phase, called the ROS algorithm. We performed extensive experimental evaluations of both approaches. Our results show that the tree-based approach using the TFM tree significantly outperforms the TC-approach.
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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 | 0.001 | 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".