Wireless home area networks in smart grids: Modelling and delay analysis
Bibliographic record
Abstract
Several interconnected layers of network form the smart grid. The Home Area Network (HAN) is the lowest layer of this grid, where reports about the various parameters associated with electrical devices are generated. For example, parameters could include information about the operation, power consumption, energy losses, power factor, etc. associated with the electrical devices. The reports generated are often classified as either periodic or critical. The critical reports are required to be communicated to a control station with minimal delay so that corrective measures could be taken quickly at the site of the electrical device. This paper presents the modelling and delay of wireless HANs. The communication between electrical devices and their associated access point can be achieved using several architectures, such as Frequency Division Multiple Access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA). For each of these multiple access techniques upper and lower bounds on delay are derived. These bounds are a function of Signal-to-Noise Ratio (SNR), channel interference range, and the number of electrical devices and channels. It is noted that transmission of critical reports from electrical devices to their access point under the CDMA scheme achieves the least delay among the three multiple access techniques considered in the paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".