Optimal power consumption control of sensor node based on (<i>N, D</i>)-policy discrete-time queues
Bibliographic record
Abstract
In this paper, we consider two types of power consumption control policies for the long lifetime of wireless sensor node based on the discrete-time Geo/G/1 queue. One is the max(N, D)-policy, which triggers transmission mode of radio server when the N and D policies are met simultaneously, and another is the min (N, D)-policy, which restarts transmission function of radio server when either of the N and D policies is first satisfied. Under two control policies, the steady-state queueing analysis of sensor node is mathematically carried out. The mean queueing measures of sensor node, such as the mean number of data packets, mean transmission time backlog, mean waiting time, mean busy period, mean busy cycle period, and so on, are derived. Two power consumption functions are constructed through the queueing measures obtained. Numerical experiments validate that two policies are feasible and efficient for power consumption control of sensor node. At a minimum power consumption, the superiority of the N-policy, D-policy, and two dyadic (N, D) policies is numerically compared. Some practical insights on the operation of two (N, D) polices in power consumption control of sensor node are obtained.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".