GNB-RPL: Gaussian Naïve Bayes for RPL Routing Protocol in Smart Grid Communications
Bibliographic record
Abstract
This study investigates the potential of utilizing the Gaussian Naive Bayes algorithm for enhancing the performance of the Wireless Smart Grid Networks (WSGNs). We have incorporated the Gaussian Naive Bayes algorithm into the widely used Routing Protocol for Low-Power and Lossy Networks (RPL), resulting in an advanced variant named as GNB-RPL. This innovative protocol leverages the Naive Bayes algorithm to optimize routing decisions. Training a Naive Bayes classifier model on a data set of routing metrics enables us to make predictions about the probability of successfully reaching a destination node. Each network node utilizes this classifier to select the route with the highest probability of delivering packets effectively. Our findings demonstrate that GNB-RPL significantly enhances the packet delivery ratio while minimizing end-to-end delay through a comprehensive performance evaluation conducted in a realistic scenario and across different traffic loads. These results show the potential of GNB-RPL as a promising solution for achieving greater efficiency in WSGNs.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| 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".