Time slot allocation in WSNs for differentiated smart grid traffic
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) are emerging as promising tools to aid in monitoring the smart grid. WSNs can be easily deployed in a wide number of smart grid assets such as substations, overhead power lines and power generation sites. They offer ubiquity and flexibility at a low-cost. However it may be difficult to offer the Quality of Service (QoS) demanded by the smart grid applications with WSNs since sensor nodes share the same wireless communication medium. To overcome this challenge QoS-aware medium access is essential. In this paper, we propose the QoS-aware GTS Allocation (QGA) scheme which aims to reduce the end-to-end delay of delay-critical data while routing delay-tolerant data using unutilized resources. QGA is implemented for a multi-hop, mesh network topology which is the most flexible WSN deployment. In QGA intermediate level sensor nodes run an optimization model in order to perform optimum time slot allocation to minimize the delay of critical data. Meanwhile, delay-tolerant data is buffered until the delay-critical traffic passes through this relaying node. Smart grid monitoring application determined whether data is delay-critical or not based on the alarm values. For instance, a transformer overloading measurement is treated as delay-critical while an ambient measurement corresponding to room temperature is treated as delay-tolerant. We show that QGA significantly reduces the end-to-end delay of high priority traffic.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".