Quality-of-Service Differentiation for Smart Grid Neighbor Area Networks
Bibliographic record
Abstract
Neighbor Area Networks (NAN) play a crucial role in the Smart Grid Communications Network (SGCN) and provide connectivity for a vast number of smart meters (SMs) while meeting the varying Quality-of-Service (QoS) requirements of different types of Smart Grid (SG) applications. To that effect, this paper explores the incorporation of QoS differentiation at the network layer in order to shape and control traffic of multiple classes in an end-to-end manner for the SG NAN using the Routing Protocol for Low Power and Lossy Networks (RPL). In particular, this paper proposes QoS enabling extensions, RPL-M and RPL-M+, within the RPL framework by associating multiple network graph instances to different SG application requirements. For illustration, the paper considers two deployment scenarios: large-scale dense urban area and microgrid-based remote rural region. Simulation-centric performance evaluation, in terms of latency and throughput, is performed for periodic and critical traffic classes, for two distinct grid conditions, with increasing data traffic load. The rural region is chosen to highlight achievable performance for microgrids and the transferability of results to different grid architectures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".