Modelling and Emulation of Multifractal Noise in Performance Evaluation of Mesh Networks
Bibliographic record
Abstract
This paper describes a model and a setup for emulating fractal and multifractal noise for the measurement and evaluation of performance of ZigBee mesh networks intended for harsh industrial environments in which wire harnesses are to be replaced with mesh networks. The main disadvantages of hard-wired signal and data communications in industrial applications include the difficulty of troubleshooting system faults, and endangering human lives when a harness malfunctions. Most industrial systems involve many sensors to enable system control and monitoring. The need to replace such wire harnesses and to acquire signals from a large number of sensors necessitates a wireless approach with ZigBee mesh networking, where nodes can self-heal and self-form. ZigBee is a short-range wireless networking technology that builds upon the IEEE 802.15.4. It came about in response to industry needs for a standard-based wireless protocol that is low power, low cost, reliable, and interoperable for low data rate industrial control and monitoring applications. In addition to the modelling of noise, this paper also presents an overview of ZigBee in terms of its targeted application spaces, the device types, how mesh networking is supported, as well as the sources and types of interference
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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 | 0.001 | 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".