Resource Management Approach to an Efficient Wireless Sensor Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Measurements of energy consumption is an important prerequisite in the development of Wireless Sensor Network (WSN) applications. WSNs are usually deployed to remote locations to monitor physical phenomena such as humidity, temperature, and pressure. The sensors in WSN applications are powered by batteries. The lifetime of these sensors and the overall functionality of the WSN relies on the lifespan of the batteries. Redundancy is experienced in the WSN when many sensors are deployed to an area to monitor a phenomenon. Redundancy leads to energy wastage as such the lifetime of the WSN is negatively impacted. Towards this end, this paper proposes an approach to reduce redundancy, achieve efficiency, and then extend the lifetime of the sensors in a WSN. The approach is the introduction of a Resource Management Algorithmto the WSN. To demonstrate feasibility of this approach, TinyOS Simulator is utilized.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.003 |
| 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