Software Defined Networks based Smart Grid Communication: A\n Comprehensive Survey
Bibliographic record
Abstract
The current power grid is no longer a feasible solution due to\never-increasing user demand of electricity, old infrastructure, and reliability\nissues and thus require transformation to a better grid a.k.a., smart grid\n(SG). The key features that distinguish SG from the conventional electrical\npower grid are its capability to perform two-way communication, demand side\nmanagement, and real time pricing. Despite all these advantages that SG will\nbring, there are certain issues which are specific to SG communication system.\nFor instance, network management of current SG systems is complex, time\nconsuming, and done manually. Moreover, SG communication (SGC) system is built\non different vendor specific devices and protocols. Therefore, the current SG\nsystems are not protocol independent, thus leading to interoperability issue.\nSoftware defined network (SDN) has been proposed to monitor and manage the\ncommunication networks globally. This article serves as a comprehensive survey\non SDN-based SGC. In this article, we first discuss taxonomy of advantages of\nSDNbased SGC.We then discuss SDN-based SGC architectures, along with case\nstudies. Our article provides an in-depth discussion on routing schemes for\nSDN-based SGC. We also provide detailed survey of security and privacy schemes\napplied to SDN-based SGC. We furthermore present challenges, open issues, and\nfuture research directions related to SDN-based SGC.\n
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".