Quality-of-service-aware fiber wireless sensor network gateway design for the smart grid
Bibliographic record
Abstract
Smart grid aims to enhance the efficiency, security and the reliability of electricity generation, delivery and consumption. Smart grid benefits from a variety of Information and Communication Technologies (ICTs) for the betterment of the power grid. Two-way communications between the customers and the utility, advanced monitoring tools and intelligent control mechanisms are the key components to realize the new services of the smart grid. Particularly, energy monitoring tools at the customer premises as well as event and ambient monitoring tools at the substations, power lines and vaults play a significant role in managing and protecting the smart grid. Wireless sensor network (WSN) technology is a promising monitoring tool for residential premises and the smart grid assets. However, the large volume of data collected by billions of sensors requires a robust communication infrastructure to deliver data from the field to the operators in a timely manner. In this paper, we adopt the Fiber-WSN architecture to support both WSN data and Fiber To The Home/Building/Curb (FTTX) traffic. We design a Fi-WSN gateway that allows data prioritization, maintains the Quality of Service (QoS) of FTTX users and delivers WSN data in a reliable manner. Data prioritization is fundamental for design considerations since ambient data have lower priority than an alarm generated at a smart grid asset. Our gateway employs a burst assembly mechanism that allows differentiation between high and low priority packets in the Fiber-WSN architecture. We show that the proposed gateway design attains low delay for high priority packets while maintaining the delay of FTTX traffic and the reliability of the WSN at desired levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".