End‐to‐end delay and packet drop rate performance for a wireless sensor network with a cluster‐tree topology
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Bibliographic record
Abstract
ABSTRACT In this paper, we study the delay performance in a wireless sensor network (WSN) with a cluster‐tree topology. The end‐to‐end delay in such a network can be strongly dependent on the relative location between the sensors and the sink and the resource allocations of the cluster heads (CHs). For real‐time traffic, packets transmitted with excessive delay are dropped. Given the timeline allocations of each CH for local and inter‐cluster traffic transmissions, an analytical model is developed to find the distribution of the end‐to‐end transmission delay for packets originated from different clusters. Based on this result, the packet drop rate is derived. A heuristic scheme is then proposed to jointly find the timeline allocations of all the CHs in a WSN in order to achieve the minimum and balanced packet drop rate for traffic originated from different levels of the cluster tree. Simulation results are shown to verify the analysis and to demonstrate the effectiveness of the proposed CH timeline allocation scheme. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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