Priority- and Delay-Aware Medium Access for Wireless Sensor Networks in the Smart Grid
Bibliographic record
Abstract
Monitoring smart-grid assets in a timely manner is highly desired for emerging smart-grid applications such as transformer monitoring, capacitor bank control, plug-in hybrid-electric-vehicle load management, and power quality assessment. Wireless sensor and actor networks (WSANs) are anticipated to be widely utilized in a wide range of smart-grid applications due to their numerous advantages along with their successful adoption in various critical areas including military and health. For resource-constrained WSANs, transmitting delay-critical data from smart-grid assets calls for data prioritization and delay responsiveness. In this paper, we introduce two medium-access approaches, namely, delay-responsive cross-layer (DRX) data transmission and fair and delay-aware cross-layer (FDRX) data transmission, which aim to address the delay and service requirements of smart grids. DRX is based on delay-estimation and data-prioritization steps that are performed by the application layer, in addition to the MAC layer parameters responding to the delay requirements of the smart-grid application and the network condition. On the other hand, FDRX incorporates fairness into DRX by preventing a few nodes from dominating the communication channel. We provide a comprehensive performance evaluation of those approaches. We show that DRX reduces the end-to-end delay while FDRX has lower collision rate compared with DRX. We outline the tradeoffs regarding these approaches and draw future research directions for robust communication protocols for smart-grid monitoring applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".