Robustness of the routing protocol for low-power and lossy networks (RPL) in smart grid's neighbor-area networks
Bibliographic record
Abstract
Neighbor-area network (NAN), also known as smart meter communication network, is one of the most important constitutive segments of smart grid communication network. Since almost all smart meters are deployed in hash outdoor environment, they could fail or wireless links between them could be fluctuating over time. These dynamics could hinder the network connectivity and degrade the reliability of data communications. However, the robustness of NANs in the case of network element failures has not received sufficient attention in existing work. This paper therefore proposes a cross-layer scheme that adaptively switches preferred parent nodes in order to help the routing protocol for low-power and lossy networks (RPL), the state-of-the-art implementation of self-organizing routing class, quickly deflect network traffic from points of failures in the NAN scenario. Operation and performance of the proposed scheme in IEEE 802.11-based wireless mesh NANs are studied by simulations.
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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".