A delay mitigation scheme for WSN-based smart grid substation monitoring
Bibliographic record
Abstract
The Quality of Service (QoS) in smart grid communications especially in monitoring smart grid assets is becoming significantly important for emerging smart grid applications. Wireless Sensor Networks (WSNs) are expected to be widely utilized in a broad range of smart grid applications due to their numerous advantages along with their successful adoption in various critical areas including military and health. WSNs protocols are not designed to provide QoS provisioning for monitoring applications. Thus, the use of WSNs in transmitting delay-critical data from smart grid assets calls for data prioritization and delay-mitigation schemes. In this paper, we propose a delay-responsive, cross layer scheme with linear backoff (LDRX) mechanism to address delay and service requirements of the smart grid monitoring applications. The LDRX scheme is designed to operate in cluster-tree WSN topology that is suitable for monitoring wide areas such as electrical substations or large installations. We show that LDRX has greater impact on delay reduction compared to previously proposed WSNs delay reduction schemes.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".