Stochastic modeling of distributed,dynamic,randomized clustering protocols for wireless sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: Distributed clustering architecture has been considered an effective and practical model to offer energyefficient, load-balancing, scalable, and robust communication for Wireless Sensor Networks (WSNs). In this paper, we compare and analyze various clustering schemes based on a comprehensive classification. We propose a bi-dimensional Markov chain model for analyzing a class of distributed, dynamic, and randomized (DDR) clustering schemes. With this model, we present extensive evaluation of stochastic properties of a representative DDR clustering scheme – Low Energy Adaptive Clustering Hierarchy (LEACH), in terms of the distribution of cluster number, the mean, the standard deviation and coefficient of variation of number of clusters. The results indicate that the number of clusters generated in LEACH-like DDR schemes is a random variable, which can not concentrate with in a narrow range of the optimal value. This variability in the number of clusters adversely affects the system lifetime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it