An Optimized WSN Design for Latency-Critical Smart Grid Applications
Bibliographic record
Abstract
The growing popularity of the Internet of Things (IoT) systems such as the smart grid, Body Area Networks (BANs), and the Intelligent Transportation System (ITS) is driving Wireless Sensor Network (WSN) systems to the limit in terms of abilities and performance. WSNs were initially designed for low power, low data rate, and latency-tolerant applications. However, this paradigm is changing because of the nature of the new applications. Therefore, instead of only focusing on power-efficient WSN design, researchers and industries are now developing Quality of Service (QoS) protocols for WSNs. In addition to that, latency- and reliability-critical protocol designs are also becoming significantly important in WSNs. In this paper, we present an overview of some important smart grid latency-critical applications and highlight WSNs implementation challenges for these smart grid applications. Furthermore, we develop and evaluate two novel optimization models that solve for the optimum values of the end-to-end latency and power consumption in a clustered WSN given lower bounds on reliability and other network parameters.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".