Energy-efficient On-demand Active Contour Service for sensor networks
Bibliographic record
Abstract
Contour mapping is an important technique for Wireless Sensor Networks (WSNs) in environmental monitoring to abstract the information of a monitored field. State-of-the-art approaches for contour mapping, however, are neither energy-optimized, nor capable of handling heterogeneous user requests. In this paper, we develop a novel energy-efficient On-demand Active Contour Service (OACS) for power-constrained WSNs. OACS regresses the field intensity function with kernel Support Vector Regression (SVR), a novel machine learning tool that flexibly handles both contour line and contour map requests. OACS also adaptively accommodates a wide range of contour line/map precision requirements: (1) For applications of low precision, only a minimum set of nodes are scheduled in working mode while others are sleeping for conserving energy. (2) For applications of high precision, through an active and progressive learning algorithm, OACS determines the best set of nodes that should be turned on for improving the contour line/map precision. Evaluation based on diverse realistic models demonstrates that OACS provides quality and seamless contour services for various application requirements yet significantly conserves energy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".