Bibliographic record
Abstract
Energy efficiency advancement is vital in target tracking Wireless Sensor Networks (WSNs) due to resource constraints of tiny detectors. On the other side, Tracking quality must also be assured while minimizing energy consumption. The main aim of target tracking algorithms is to depict the trajectory of the target at a sink node using the aggregated data from all sensor nodes. Monitoring an Area of Interest (AoI) while other sensors are in sleep mode has been shown to effectively improve energy efficiency in WSNs. In this paper, we propose a VAriable Radius Sensor Activation (VARSA) algorithm to decrease the sensing energy consumption of tracking applications using WSNs. VARSA uses a dynamic sensing radius adjustment and sends the appropriate sensing radius to the next predicted sensor to wake up with. In addition to sending sensors into sleep mode when they are not in the AoI, we propose to decrease the sensing radius of the sensors in the AoI in real time to further decrease the consumed energy of sensing. Simulation results demonstrate that the proposed algorithm significantly decreases the sensing energy consumption, while providing better tracking quality over time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.116 | 0.090 |
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".