Attainable throughput, delay and scalability for geographic routing on Smart Grid neighbor area networks
Bibliographic record
Abstract
Challenges of the existing power grid demand the integration of information and communication technologies into the next-generation electric grid, namely the Smart Grid (SG). This paper focuses on the critical communications segment corresponding to the consumer-premise, the Neighbor Area Network (NAN). For this segment, Greedy Perimeter Stateless Routing (GPSR) protocol is considered for its low complexity and high scalability. In order to provide guidelines for SG communications system designers and network engineers, the performance of GPSR in terms of throughput, latency and scalability is investigated with parametric sweeps of transmission range, data rate and the number of Smart Meters (SMs) per Data Aggregation Point (DAP). The simulation results show that network throughput of hundreds of times the rate required for basic SG applications (e.g., meter reading, service switch, …) can be achieved while the end-to-end delay can always be maintained below 100 ms. However, the converge-cast nature of the uplink traffic severely limits the SM-to-DAP ratio. Thus, with GPSR at the NAN level, emerging SG applications such as smart metering, real-time pricing, demand response, etc., can be supported.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".