Sensor network placement for maximizing detection of vehicle tracks and minimizing disjoint coverage areas
Bibliographic record
Abstract
The effective placement of intelligence, surveillance and reconnaissance (ISR) assets in a sensor network for optimal situational awareness is an important area of military operations research. The sensor network placement optimization problem is a type of assignment problem where possible sensor locations can be assigned sensors of different types (or no sensor). We apply a genetic algorithm to the sensor network node assignment problem, with the goal of optimizing for barrier, rather than blanket, coverage. This shifts the measure of effectiveness of the network from the amount of area coverage to continuity of coverage and the number of incoming targets detected. To address this problem, we simulate vehicle tracks crossing a nominal area of interest. We then apply the Non-dominated Sorting Genetic Algorithm II (NSGA II) to determine the placement of sensors with the following objectives: to maximize the number of detected vehicle tracks, to minimize the number of discontinuities in coverage, and to minimize total sensor network cost. In this paper, we describe the sensor network node assignment problem, and our specific implementation of NSGA II. We then present a specific test scenario and a preliminary example of the generated non-dominated front including various sensor network configurations. Finally, we outline future work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".