Effective Lifetime-Aware Routing in Wireless Sensor Networks
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Bibliographic record
Abstract
Lifetime-aware routing and desired sensing spatial coverage (SSC) are two main challenges ahead of an ad-hoc, sensor-based, battery operated monitoring system known as wireless sensor network (WSN). Depending on the application, a necessary SSC level is essential to comply with the needed surveillance quality. On the other hand, network lifetime is of a major concern due to limited energy available to each sensor node. Formerly proposed lifetime-aware routing algorithms have usually defined lifetime as the duration before the first node runs out of energy. This criterion is not consistent with real-world WSN, where a number of sensors are likely to “die” due to hardware failures, natural impacts, etc. Initially, we propose a method that determines the network resource specifications, i.e., the number of available nodes and their sensing range, according to the required SSC and the necessary confidence level. Later on, a novel lifetime criterion which considers the SSC as the WSN effective operation criterion is introduced. Afterward, the new criterion is embedded into the flow augmentation algorithm and the normalized network lifetime is calculated for several scenarios using the solutions of the corresponding linear programming equations. Simulation results show significant improvement achieved in the lifetime. It is also shown that this new method is considerably more robust to the routing algorithm parameters compared to the performance achieved by the published Flow Augmentation algorithm.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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