A Nested Genetic Algorithm-Based Optimized Topology and Routing Scheme for WSN
Bibliographic record
Abstract
Wireless sensor networks (WSN) are majorly applied in recent times. Sensors are deployed in several areas to collect different kinds of data. Sensor nodes are low power and run out of energy quickly. When a sensor node runs out of energy, depending on the deployed environment, it can be quite difficult to replace it. Therefore, we need to prolong the lifetime of the WSN as long as possible. Genetic algorithm (GA) is a meta-heuristic algorithm that has been applied in many optimization problems. This paper presents a nested GA approach that provides the sink node location, selects cluster heads and computes the inter-cluster heads routing simultaneously significantly improving the network lifetime. We compare our algorithm with four other recent works conducted in the research space, and our method achieves average 15% better performance than the best in normal conditions and 30% better performance in more lossy environments.
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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 | 0.000 | 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".