Optimal Resource Allocation For QOS Supports In Smart Grid Neighborhood Area Network
Bibliographic record
Abstract
Smart grid is a utility network, with advanced information and communications technologies for improved control, efficiency, reliability and safety in electric power distribution and management. Smart grid communication network consists of three interconnected communication networks: home area network (HAN), neighborhood area network (NAN), and wide area network (WAN). Our thesis is focused on NAN. The information flow in smart grid communication networks has different Quality of Service (QoS) requirements in terms of packet loss rate, throughput, and latency. By deploying QoS mechanisms, we can get the real time feedbacks which can be used to supply electricity based on need, thus reducing the wastage of electricity. First, we conducted Opnet simulations for NAN. We evaluated two technologies, Zigbee and wireless local area network (WLAN), for NAN. The simulation results demonstrate that latency can be reduced for the data flow with a higher priority with an appropriate QoS mechanism. Next, we proposed an optimal resource allocation scheme to reduce delay and provide differentiated services, in terms of latency, to different classes of traffic in the NAN. The problem is formulated into a linear programming (LP) problem, which can be solved efficiently. The simulation results and comparison demonstrates that the proposed resource allocation scheme can provide overall lower latency of the various data flows. Our method also lowers the delay of the data flow with a higher priority.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".