Performance Evaluation of Single-Board Embedded Linux Platforms as Asterisk Servers for Phone of Things (PoT) Applications
Bibliographic record
Abstract
Phone of things (PoT) is a novel idea for loT systems connectivity that allows legacy and non-loT-enabled devices to be interfaced through the mature and ubiquitous telephone network infrastructure. This paper evaluates the performance of the Raspberry Pi board families, embedded Linux platforms based on ARM processors, on processing VolP calls for PoT applications. The paper assesses and contrasts the ability of the boards in handling passthrough and trans coded VolP calls. Based on the obtained results, the paper proposes best practices for employing the boards as Asterisk servers and advocates the maximum number of simultaneous calls at different scenarios to preserve the hardware safety and maintain better performance regarding system stability and VolP call quality measurements. The results show that Raspberry Pi 4 B can gracefully han-dle up to 364 active passthrough channels (equivalent to 182 simultaneous calls). Nevertheless, Pi Zero W, the least powerful version of the Raspberry Pi, can gracefully handle up to 24 active passthrough channels. The results promote the utilization of embedded Linux platforms as appropriate, tiny form factor, and cost-effective candidates to act as PoT gateways in homes and small-to-medium sized business domains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".