PIoT: A Performance IoT Simulation System for a Large-Scale City-Wide Assessment
Bibliographic record
Abstract
Given the proliferation of sensors and actuators, evaluating the network performance of current and forthcoming city-wide Internet of Things (IoT) applications is a challenging task. To overcome this challenge, we created a large-scale simulator called PIoT over the years that can assess the performance of multiple millions of mobile/static IoT devices using a 4G/5G cellular infrastructure. In PIoT, different Key Performance Indicators (KPIs) are defined and collected to produce data to evaluate applications and network performance. PIoT is an on-going academic simulator project, and its most recent version is accessible to the public through the user interface found on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://www.piotsimulation.com</uri> without any installation requirements. It uses a realistic database that contains the real locations and features of Base Stations (BSs) and the real locations of IoT user equipment devices. The interface can be used by operators and researchers to understand network behavior when deploying new applications or to gather data to feed artificial intelligence and machine learning algorithms. The objective of this paper is to present a detailed description of the PIoT modeling architecture, as well as some use cases to help potential users understand the type of capabilities that are available when using the simulator. Limitations and comparisons with other popular engines are also included in the paper.
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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".