Spatiotemporal Adaptive Optimization of a Static-Sensor Network via a Non-Parametric Estimation of Target Location Likelihood
Bibliographic record
Abstract
The search for a mobile target is a dynamic spatial and temporal problem. Information gathered during the search, regarding the potential whereabouts of the target, can be used to influence significantly the search strategy employed. In this paper, thus, a novel (wireless) static-sensor network deployment strategy is presented to detect efficiently and effectively a mobile target in an unbounded environment. The proposed strategy deploys the static sensors optimally and in a time-phased manner while adapting in real-time to the availability of new information during the search. An optimal network-deployment plan, herein, refers to a set of optimal sensor-deployment times and locations. The optimal sensor-deployment instances aim to achieve a uniform deployment of search effort over time. Optimal sensor-deployment locations, in turn, are determined according to the highest possible likelihood of detection of the mobile target. The proposed deployment strategy contains two novel contributions: it makes use of a non-parametric approach to estimate effectively the target location likelihood, and it performs an optimization of sensor-placement times to maximize the adaptive characteristic of the deployment plan. Several detailed experiments (in virtual and physical environments) of the proposed strategy for static-sensor network deployment in wilderness search and rescue applications are presented. Furthermore, a comparative study is included to highlight the advantages of our approach versus traditional methods that deploy sensors simultaneously for uniform coverage.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".