Performance analysis of large scale RF-mesh networks for smart cities and IoT
Bibliographic record
Abstract
Smart Cities, IoT and Smart Grid development needs solid and steady communication infrastructures to support the diversity and high number of applications. RF-mesh is one of the most popular wireless technology in those context since it allows to connect a large number of nodes in a mesh topology. To study the suitability of a network infrastructure for the applications requirement, network performance analysis is needed. This paper proposes a novel Markov-modulated model for the performance analysis of large scale RF-mesh networks for smart cities applications. The instances are build using Geographical Information System (GIS) data in order to maintain fitness to actually implemented RF-Mesh systems. The model is characterized by a high scalability (e.g., thousands of nodes) and a limited computational time (i.e., in the order of some minutes). The model also takes into account implementation details, such as retransmission probability, buffer size and a precise wireless interference analysis, which are not thoroughly considered in previous RF-Mesh performance literature.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".