Evaluation of an efficient Smart Grid communication system at the neighbor area level
Bibliographic record
Abstract
The successful implementation of Smart Grid (SG) requires an efficient communication infrastructure that is cost-effective, scalable and fault-tolerant. This paper aims to study and develop relevant networking techniques for an efficient and reliable SG Communication Network (SGCN). In particular, we propose a viable communication architecture for the interconnection of different radio access technologies along the separate segments of the SGCN. Specifically, WiFi mesh network at the Neighbor Area Network (NAN) level with LTE at the Wide Area Network (WAN) level. Based on this architecture, the performance, transmission latency and Packet Delivery Ratio (PDR), of geographic routing in the NAN segment is considered. Specifically, the scaling of system performance when per-smart-meter data rate, channel shadowing level and the number of smart meters per collector increases is investigated. The results presented in this study can then serve as important guidelines for the design and development of relevant communication infrastructures for SGs.
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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 | 0.002 | 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".