Pruning and Validation Techniques Enhanced Genetic Algorithm for Energy Efficiency in Wireless Sensor Networks
Bibliographic record
Abstract
Designing energy-efficient systems in Wireless Sensor Networks (WSNs) is challenging as each sensor has limited energy.This research paper suggests a combined method that merges a Genetic Algorithm (GA) with pruning and validation strategies to enhance sensor network routing paths to minimize energy usage.The GA uses variable-length chromosomes to depict paths from a source sensor node to a sink node.Initial populations are created randomly and genetic mechanisms such as selection, crossover, and mutation are applied to refine these paths for efficiency.Pruning methods are then used to remove redundant nodes in the obtained paths ensuring energy-efficient routing.Path validation in the GA processes ensures that each path adheres to the transmission range limits of sensors.The experiments use setups with 20, 50, 100, and 150 sensor nodes.Results have shown that this approach chooses the best paths with minimal energy consumption and it is superior to the Ant Colony Optimization (ACO) algorithm.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".