Ad hoc wireless network implementing BEE-LEACH
Bibliographic record
Abstract
Adaptations have been key to the development and advancement of the low energy adaptive clustering hierarchy (LEACH) protocol. Presented is an alteration to the traditional LEACH, low energy adaptive clustering hierarchy, algorithm. This algorithm focuses on the battery life optimization of sensors within a wireless sensor network (WSN). These sensors will be grouped into clusters with the aim of maximizing the battery life of the overall system by sorting each sensor by residual energy and assigning the highest residual energy the role of cluster head. The protocol will then assign sensors to cluster heads based on distance relative to the head. This algorithm achieves the goal of extending battery life and offers itself as a promising alternative to standard LEACH algorithms. The algorithm is tested by comparing sensor battery life, total sensors communicating at a given time, and sensors with residual energy. This paper addresses the strengths and vulnerabilities of the algorithm, as well as proposed work for further implementation for the following groups looking to create their own LEACH protocol.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".