Genetic Algorithm for Energy Efficiency in Wireless Sensor Networks
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are a popular solution for several monitoring applications, due to their low cost and ease of deployment, while they can collect a plethora of data. To acquire such information WSNs consist of distributed sensor nodes, usually inch scale with limited energy resources. Since WSNs are formed in an ad hoc manner, the distribution of sensor nodes in a geographic location may not be known prior to the deployment of the nodes, hence, routing paths between nodes and a base station should be found. Finding routing paths requires energy while even with routing paths established, transmitting data between nodes will increase the total energy consumption in the network. When a node runs out of energy it may no longer be used as part of a routing path. Therefore conserving energy will prolong the node lifespan, increasing the lifespan of the WSN. In this work, a Genetic Algorithm (GA) is used as a clustering strategy to improve energy efficiency in a large-scale WSN. According to simulation results, GA can achieve better performance in terms of energy consumption and is a promising approach to extend the network lifespan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".