Minimum Cost Placement of Bistatic Radar Sensors for Belt Barrier Coverage
Bibliographic record
Abstract
How to construct barrier coverage efficiently is a critical problem for many wireless sensor network applications, such as boundary surveillance and intrusion detection. In this paper, we study the belt barrier coverage in bistatic radar sensor networks. Much different from the disk and sector coverage, the coverage area of bistatic radar is dependent on the distance between a pair of radar transmitter and receiver. To improve coverage quality, we require to construct a belt barrier with the breadth not smaller than a predefined threshold. Furthermore, the unit cost of a radar transmitter may be different from a receiver. The bistatic radar placement problem is to construct a belt barrier with the minimum total placement cost. To solve the minimum cost placement problem, we propose a line-based equipartition placement strategy such that all radars placed on a deployment line can form a barrier with some breadth and one or more such placement lines can form a belt barrier with the required breadth. We first study the barrier property of different placement patterns on one deployment line, and prove the structure property of the optimal placement sequence on one deployment line. When multiple deployment lines are needed for belt barrier construction, we propose algorithms to find out the number of deployment lines and the number of receivers in the optimal placement pattern on each deployment line to minimize the total placement cost. The efficiency of the proposed algorithm is also validated by our simulation 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".