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 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".