Performance of TDMA scheduling algorithms in the presence of data correlation in sensor networks
Bibliographic record
Abstract
TDMA scheduling for data gathering in wireless sensor networks can potentially save energy by eliminating collisions and avoiding idle listening due to its built in duty cycle. Furthermore, temporal and spatial correlation in the sensed data gives room for better delay and energy efficiency. Several TDMA scheduling schemes have been suggested in the literature. However the impact of data correlation on those schemes is not widely reported. In this paper we study the effect of data aggregation on energy and delay performance of two scheduling schemes, namely, interleaved and non-interleaved scheduling. Through simulation we show that non-interleaved scheduling utilizes data aggregation more efficiently to reduce its delay by a factor of 2.13 to 4.9 compared to interleaved scheduling. However, its overall energy savings is minimal due to its short duty cycle. Interleaved scheduling shows a balanced performance in terms of energy and delay at different levels of data correlation. That could make it a more desirable choice for a wider range of sensor networks applications.
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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 | 0.004 | 0.018 |
| 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 | 0.000 |
| 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 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".