A Dynamic Approach to Sensor Network Deployment for Mobile-Target Detection in Unstructured, Expanding Search Areas
Bibliographic record
Abstract
This paper proposes a novel strategy for the deployment of a static-sensor network based on the use of a target-motion probability model. The focus is on the real-time dynamic and optimal deployment of the network for detecting untrackable targets. The dynamic nature of the deployment refers to the on-line reconfigurability of the network as real-time information about the target becomes available. The optimal locations of the network nodes, in turn, are determined based on maximizing the probability of finding the target through the use of iso-cumulative-probability curves. The proposed strategy is adaptable to unstructured environments with natural terrain variation and the presence of obstacles. Extensive simulations, some of which are included in this paper, verified the advantage of our deployment strategy over other existing methods. Namely, the proposed strategy can tangibly increase the success rate of target detection, while reducing the mean detection time, when compared with uniform-coverage-based approaches that do not consider probabilistic target-motion modeling. A comprehensive example is also included, herein, to illustrate the successful application of our proposed deployment strategy to a wilderness search and rescue scenario, where both static and mobile sensors are employed within a hybrid sensor-deployment strategy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".