The role of queueing theory in the design and analysis of wireless sensor networks: An insight
Why this work is in the frame
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Bibliographic record
Abstract
Most research on the mathematical modelling of wireless sensor networks (WSNs) have focussed mainly on the optimization aspects, such as those relating to sensor placements, data routing, reliability, etc. Surprisingly the issue relating to performance analysis of data processing and transmission at the nodes, have not received as much attention. A considerable amount of delay to data actually happens at the nodes as a result of queue build up. Hence, understanding the role of queueing in WSN modelling is very important. In this paper we study the literature of queueing as applied to WSNs and provide insight to the current state of the art and directions for the future. The utilization of queueing theory in WSNs is broadly classified into four categories, namely, congestion control methods, power allocation schemes, network performance evaluation techniques and scheduling schemes.
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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 | 0.001 |
| 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 it