Achieving High QoS in Smart Grids Through Priority-Based Data Transmission
Bibliographic record
Abstract
In the smart grid communication, the Neighborhood Area Network (NAN) plays an important role to exchange information between utilities and huge number of smart meters (SMs). The major challenge in a wireless NAN is to maintain the Quality of Service (QoS) parameters such as throughput and delay. In this paper, a priority based relaying of smart meter data is proposed for enhancing the QoS parameters by segregated data from high priority and low priority. As the high priority data packets carry critical information from the smart meters, we carried out priority-based relaying to achieve the effectiveness for high priority data delivery to data concentrator. The proposed framework for priority-based relaying for wireless NAN is simulated using OPNET, which shows the enhancement in QoS parameter. Machine learning is proving the most promising tool for traffic prediction and the integration of machine learning for the traffic prediction in smart grid environment. A limited number of work has been published in smart grid which integrates the current available technology such as third generation of network wireless protocol (3G/WiMAX) and LTE-A protocol, WSN, ZigBee, hybrid routing method and opportunistic routing mechanism to enhance different aspects of smart grid network.
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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".