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 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".