Genetic Algorithm-Based Routing Performance Enhancement in Wireless Sensor Networks
Bibliographic record
Abstract
This paper presents a series of different routing techniques to be implemented in a wireless sensor network and we consider two main cases. In the first case, we tested both Dijkstra algorithm (DA) and genetic algorithm (GA). In the second case, we have tested and compared the traditional Ad hoc On-Demand Distance Vector Routing protocol (AODV) to the advanced genetic algorithm based AODV Routing protocol (GA-AODV) and the GA only. GA and GA-AODV techniques help in enhancing the performance of the wireless sensor network during link failures. In this case, we have tested routing algorithms while it is considered having faulty nodes to be 15.6% and 31.25% of the functioning nodes. We use simulation to test the algorithms while assuming different mobility speeds of the nodes. Results prove that GA should be used in different network configurations to obtain a better performance in the wireless sensor network.
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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".