Theoretical analysis of the target detection rules for the UAV-based wireless sensor networks
Bibliographic record
Abstract
A wireless sensor network (WSN) is usually deployed in a field of interesting (FoI) for detecting or monitoring some special events. Traditionally, the WSN requires intensive deployment in which the extra sensor nodes are deployed to achieve the required coverage level. While, depending on the developments of the unmanned aerial vehicle (UAV) techniques, the UAV has been widely adopted in both military and civilian applications. Comparing with the traditional mobile sensor nodes, the UAV has much faster moving speed, longer deployment range and relative long serving time. Consequently, the UAV can be considered as a perfect carrier for the existing sensing equipment and used to form a UAV-based WSN (UWSN). Naturally, in order to determine the efficiency of a UWSN, a question, “what is the probability of detecting a target in the Fol, if an UAV randomly scanned the FoI n times”, is raised. To solve this question, in this article, we theoretically analysed the target detection problem in the UWSN by considering static target and mobile target, respectively. The experimental results further verified our theoretical results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".