Modeling and Analysis of Energy Conservation Scheme Based on Duty Cycling in Wireless Ad Hoc Sensor Network
Bibliographic record
Abstract
In sensor network, energy conservation is one of the most critical issues since sensor nodes should perform a sensing task for a long time (e.g., lasting a few years) but the battery of them cannot be replaced in most practical situations. For this purpose, numerous energy conservation schemes have been proposed and duty cycling scheme is considered the most suitable power conservation technique, where sensor nodes alternate between states having different levels of power consumption. In order to analyze the energy consumption of energy conservation scheme based on duty cycling, it is essential to obtain the probability of each state. In this paper, we analytically derive steady state probability of sensor node states, i.e., sleep, listen, and active states, based on traffic characteristics and timer values, i.e., sleep timer, listen timer, and active timer. The effect of traffic characteristics and timer values on the steady state probability and energy consumption is analyzed in detail. Our work can provide sensor network operators guideline for selecting appropriate timer values for efficient energy conservation. The analytical methodology developed in this paper can be extended to other energy conservation schemes based on duty cycling with different sensor node states, without much difficulty.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".