A Software-Defined Surveillance System With Energy Harvesting: Design and Performance Optimization
Bibliographic record
Abstract
Even though energy harvesting is a promising technology for energy-efficient surveillance systems, energy harvesting levels are highly dynamic depending on the time and location. Thus, the deployment of nonenergy-harvesting sensor nodes (NHSs) and sophisticated sleep scheduling of sensor nodes are necessary for performance guaranteed surveillance systems. In this paper, we present a software-defined surveillance system (SDSS) in which a centralized controller determines the sleep schedules of energy harvesting and NHSs on the basis of the collected information such as the spatial distribution of targets and the energy levels of sensor nodes. To derive the optimal sleep schedules minimizing the number of active sensor nodes while providing sufficient surveillance performance, a constraint Markov decision process problem is formulated and the optimal policy on sleep scheduling is obtained by linear programming. The evaluation results demonstrate that the SDSS with the optimal policy can reduce energy consumption by employing fewer active sensor nodes while providing the required level of target monitoring probability.
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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 | 0.000 | 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".