Optimization of the IEEE 802.15.4 Superframe for Clustered WSNs using Differential Evolution
Bibliographic record
Abstract
Prolonging sensor and network life is a significant concern for wireless sensor networks and in this paper we present a novel optimization technique for determining the superframe slot allocation for a beacon-enabled 802.15.4 superframe operating with sensors with different data acquisition periods. The optimization approach is based on a differential evolution algorithm that leverages a flat cluster network topology to constrain the contention free period (CFP) and the contention access period (CAP) of the superframe. In this work we also define a multi-superframe structure to help conserve energy in the network. A sensitivity analysis of the optimization shows that the optimized results are not too sensitive to the clustering algorithm but are very sensitive to the original network configuration, such as number of nodes and their locations and transmission range, and therefore is better suited for static sensor networks. We also demonstrate that the optimized superframe results in a 5% gain in energy conservation over other approaches that optimize the superframe based on the ratio of the beacon order (BO) to superframe order (SO).
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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".