Delay Critical Smart Grid Applications and Adaptive QoS Provisioning
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are anticipated to be widely adopted in the various monitoring and control applications due to their versatility and low cost. One of the most promising and emerging WSNs applications is their use in monitoring smart grid assets. Although WSNs can provide cost efficient and reliable solutions, they are not suitable for delay critical application, because they were initially designed for low data rate applications and they may be challenged when sudden faults or failures occur in the monitored environments. Therefore, to prevent extensive delays of critical data, appropriate quality of service (QoS) techniques should be used. In this paper, we present an adaptive QoS scheme (AQoS) and an adaptive guaranteed time slot (AGTS) allocation scheme for IEEE 802.15.4-based WSNs used in high traffic intensity smart grid monitoring applications. Both AQoS and AGTS schemes can adaptively reduce the end-to-end delay and flexibly tune the GTS to provide the required QoS differentiation to delay critical smart grid monitoring applications.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".