Optimized Application Driven Scheduling for Clustered WSN
Bibliographic record
Abstract
The widespread use of applications through Wireless Sensor Network (WSN) demands an adaptive management system. One of the issues in autonomous WSN is finding the optimal solution for minimizing energy consumption while taking into account the application data rate requirements. This trade-off is done by modifying the size and composition of the MAC superframe. In this paper, an optimized application-aware superframe scheduling for clustered WSN is presented that optimizes the trade-off between power consumption and sensor data acquisition rate, by utilizing a differential evolution algorithm to find the optimal size and composition of the MAC superframe. The approach presented also leverages a cluster network topology in the design of the network. It also considers multi-superframe configurations that have not been previously leveraged in the literature. Simulation comparisons leveraging LEACH-C show that our approach can optimize the 802.15.4 superframe size and composition for particular sensor data rates and improve energy consumption in the network compared to using a standard single superframe.
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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".