Impact of PMU and Smart Meter Applications on the Performance of LTE-based Smart City Communications
Bibliographic record
Abstract
Electrical distribution network operators require measurements from phasor measurement units (PMUs), micro-PMUs ( μPMUs), and smart meters (SMs) in order to develop efficient distributed management system (DMS) applications. The high data-rate transmission of those measurements is a burden to the underlying communication system and its feasibility needs to be investigated. In this paper, we propose a method to characterize the traffic generated by a DMS application in a smart city scenario and an analysis of its impact on a realistic LTE infrastructure. Real geographic data on the position of SMs, PMUs, and μPMUs are employed to accurately model this DMS application and its generated traffic. A realistic LTE infrastructure is used to measure the load of DMS traffic at each eNodeB. The impact of synchronous and asynchronous DMS traffic on the LTE access is discussed, and bottlenecks in the LTE communication network are identified.
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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) | 0.000 | 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 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".