Quality of service differentiation for smart grid neighbor area networks through multiple RPL instances
Bibliographic record
Abstract
The Smart Grid (SG) optimizes the existing power grid with a reliable and secure SG Communications Network. For uplink data traffic, the Neighbor Area Network (NAN) segment's size and converge-cast traffic can be addressed with the Routing Protocol for Low Power and Lossy Networks (RPL). Additionally, a broad range of SG applications, such as monitoring, control and automation applications, have been proposed in order to achieve the anticipated SG goals. These applications, each possessing different Quality of Service (QoS) requirements such as bandwidth, latency, reliability and security, will require traffic prioritization and differentiation. Generally, QoS is addressed through queue scheduling mechanisms or prioritized channel access at the Medium Access Control (MAC) layer. In this paper, extensions to the standard MAC level QoS techniques are explored with an emphasis on network layer QoS mechanisms. Specifically, the effectiveness of multiple instances of the RPL network graph, built upon differing objective functions, for QoS differentiation is investigated. To that effect, three variants of RPL, standard RPL, multi-instance RPL (RPL-M) and multi-instance RPL with prioritized channel backoffs (RPL-M+) along with two distinct traffic classes have been examined as data traffic rate and composition was varied.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".