A Crowdsensing-based Cyber-physical System for Drone Surveillance Using Random Finite Set Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Given the popularity of drones for leisure, commercial, and government (e.g., military) usage, there is increasing focus on drone regulation. For example, how can the city council or some government agency detect and track drones more efficiently and effectively, say, in a city, to ensure that the drones are not engaged in unauthorized activities? Therefore, in this article, we propose a crowdsensing-based cyber-physical system for drone surveillance. The proposed system, CSDrone, utilizes surveillance data captured and sent from citizens’ mobile devices (e.g., Android and iOS devices, as well as other image or video capturing devices) to facilitate jointly drone detection and tracking. Our system uses random finite set (RFS) theory and RFS-based Bayesian filter. We also evaluate CSDrone’s effectiveness in drone detection and tracking. The findings demonstrate that in comparison to existing drone surveillance systems, CSDrone has a lower cost, and is more flexible and scalable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it