Optimization of Key Devices Positions in Large-Scale RF Mesh Networks
Bibliographic record
Abstract
At the present time, a great interest has been shown by the research and the industrial community concerning smart grid communications where important technical advances have arisen as a consequence. Concretely, one of the most important goals of RF mesh network is the design and development of an efficient routing protocol to connect different devices of the network between each other. Nevertheless, the overall performance of the RF Mesh network communications depends in high measure on the positions of key devices in Radio Frequency (RF) Mesh network such as routers and collectors. With this in mind, we focus our work in this paper to design a strategy to optimize the positions of the key devices of RF Mesh network such as routers and collectors to achieve the highest possible connectivity between different components of RF Mesh network. To do so, we have used a well-known clustering algorithm called Lloyd's algorithm, to design our strategy, with the clear objective of allocating routers and collectors in their optimal positions in a smart grid scenario. Extensive simulations have been carried out with the proposed strategy, and good results have been obtained.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".