Performance and applicability of geographic-based routing in smart grid's neighbor area networks
Bibliographic record
Abstract
Neighbor area network (NAN) is one of the most important segments of smart grid communications network (SGCN). This paper studies the performance of geographic-based greedy perimeter stateless routing (GPSR) in the NAN scenario and investigates the feasibility of this routing protocol in supporting S G applications. Specifically, packet transmission delay and reliability of GPSR in an IEEE 802.15.4-based wireless mesh NAN with practical system parameters are measured by simulations. The results show that, at the basic data rate, the delay can always be maintained below 70 ms (95% quartile) while packet delivery ratio is higher than 90%. This means that the simulated NAN can support a great number of S G applications including smart metering, real-time pricing, demand response, etc. However, due to that fact that more advanced applications that require information exchange at higher rates and more stringent delays are emerging in SG, the performance of GPSR in NAN scenarios using various radio technologies with higher offered loads and/or larger network scales needs to be studied.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".