Analysis of Different Sleep and Wakeup Strategies in Solar Powered Wireless Sensor Networks
Bibliographic record
Abstract
We present a novel analytical framework to investigate the performances of different sleeping strategies in a wireless sensor network where a solar cell is used to charge the battery in a sensor node. While the energy generation process (i.e., solar radiation) in a solar cell is modeled by a stochastic process (i.e., a Markov chain), a linear battery model with relaxation effect is used for the battery capacity recovery process. Average queue length, packet dropping and packet blocking probabilities and packet delay distribution at each node are the major performance metrics. Developed based on a multi-dimensional discrete-time Markov chain, the presented model can be used to analyze the performances of different sleep and wakeup strategies at each node (e.g., strategies based on available battery capacity, channel state, solar radiation condition and queue length, and hybrid of these conditions). The numerical results obtained from the analytical model are validated by extensive simulations. The presented model would be useful for designing and optimizing sleeping strategies in a solar powered sensor network under energy and QoS constraints.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".