Are ML Models Scenario-Independent in Enhancing Routing Efficiency for Smart Grid Networks?
Bibliographic record
Abstract
This study aims to achieve two objectives. Firstly, it evaluates the performance of various traditional machine learning (ML) techniques using two datasets containing Quality of Service (QoS) metrics obtained from real-world and synthetic Grid scenarios, both utilizing the Routing Protocol for Low-Power and Lossy Networks (RPL) in Wireless Smart Grid Networks (WSGNs). Secondly, it investigates how different scenarios impact the performance of ML models, considering their potential integration within the widely adopted RPL. Initially, the performance of multiple traditional ML techniques was assessed to determine the most effective one. Subsequently, two distinct models were created, one for each generated dataset, using the most effective technique. The findings highlight that the Long Short-Term Memory (LSTM) technique outperforms all other techniques, achieving an AUC (Area Under the Curve) value of >=0.98. Furthermore, an important discovery emerged from this research, indicating that the performance of the ML model diminishes when applied to a scenario different from the one it was trained on. This effect is particularly notable when transitioning from a real-world context to a simplified grid scenario.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".