Design and stochastic modeling of distributed, dynamic, randomized clustering protocols in wireless sensor networks
Bibliographic record
Abstract
In this paper, we propose a novel single hop-clustering scheme called step wise adaptive clustering hierarchy (SWATCH). SWATCH relieves the CH number variability problem by employing stepwise CH selection in two stages. It is a dynamic and straightforward scheme as LEACH. However, instead of selecting all CHs in one step, SWATCH splits the selection phase into an initial selection stage and an add-on selection stage. The initial selection is similar to LEACH. However, if the number of CHs in the initial selection is below a pre-determined target, the add-on selection will be invoked and will continue until an acceptable number of CHs have been selected. As a result, the number of CHs selected in each round tends to congregate in a narrow range around the optimal value. In order to evaluate the performance of SWATCH, we develop a hierarchical Markov chain model to track the behavior of the system. Numerical results verify our design objective in that the number of selected CHs highly conforms to the optimal value. Based on the results, an optimal number of CHs are greatly reduced the communication energy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".