Profile-based energy minimisation strategy for Object Tracking Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) are composed of power-restrained nodes that limit their lifetime, since the sources of energy are often non-replaceable. In this work, we deal with the power consumption problem in one application of WSNs: objects tracking in a monitored region. Different protocols have been proposed to minimize the energy consumption in such environments. Most of them are based in prediction techniques to know in advance the locations of the object, taking advantage of this information to switch off the sensor nodes as much as possible. However, little work has been done to utilize regularity in the object's behavior to reduce energy consumption. In this paper we propose a profile-based algorithm (PBA) that aims to use the information contained in the network and in the object itself to optimize energy consumption, thus extending lifetime. Simulations show that this method outperforms prediction-based strategies for a wide range of predictability values
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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 | 0.001 |
| 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 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".