iLEACH: An Improved LEACH Algorithm Using Residual Energy and MDC
Bibliographic record
Abstract
The classical LEACH algorithm is a well-known clustering protocol for WSNs that helps to improve the network's energy efficiency and prolong its lifetime. However, LEACH and its variants have limitations, such as the likelihood of node failure due to uneven distribution of energy consumption among the nodes. This paper proposes iLEACH, a modified version to improve the network's longevity. We propose to include residual energy as a main parameter in the cluster head formula to improve the selection process. We employ the best-fit statistical distribution to further ensure fairness and effectiveness in the selection process. This approach helps to identify the nodes with the most suitable residual energy levels for the cluster head role. Finally, to enhance the efficiency of data collection, we use a mobile data collector (MDC) that can move around the network and collect data from cluster heads, thus improving the overall energy efficiency of the network. This modification helps to balance the energy consumption among the nodes and avoid the likelihood of node failure, hence improving the network's longevity and overall performance.
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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 | 0.001 |
| 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".