A New Approach for Covering Wireless Sensor Networks with Optimum Number of Nodes in Order to Prolonging Network Lifetime
Why this work is in the frame
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Bibliographic record
Abstract
In recent years, wireless sensor networks are in great use in applications like disaster management, combat field reconnaissance, border protection and safe care. Although, much research has been done on wireless sensor networks, but in the quality of service (QoS) field there are not enough researches. Since these networks are widely used in many areas, there are different QoS parameters in contrast with traditional networks such as network coverage, optimal number of active nodes, network lifetime and energy consumption. We have proposed an automata-based scheduling method to improve the QoS parameters of the networks. In this method, each node is equipped with a learning automaton to select its correct status (active or passive) at any given time. Simulation results show that the proposed method in comparison with some existing methods such as: CCP, Lacoverage, PEAS and Ottawa reduce energy consumption and increase network's lifetime. As a result, several QoS parameters are considered in sensor networks, simultaneously.
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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 | 0.000 | 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 | 0.001 | 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 it