Rapidly-Exploring Tree With Linear Reduction: A Near-Optimal Approach for Spatiotemporal Sensor Deployment in Aquatic Fields Using Minimal Sensor Nodes
Bibliographic record
Abstract
Spatiotemporal monitoring of large fields for water quality motivates this paper. The primary goal of this paper is to find a suitable deployment strategy for mobile sensor nodes in aquatic fields, which receives the least estimation error with minimal sensor nodes. Typically, the resources of mobile sensor nodes are limited in the case of large field monitoring, so they cannot be deployed arbitrarily. Hence, an optimal sensor node deployment strategy with minimally required sensor nodes becomes a primary focus of this paper. To address this problem, an optimal sensor node deployment strategy termed Rapid Random exploring tree with Linear Reduction (RRLR) is developed in this paper. It relocates sensor nodes to their best sensing locations while reducing the required number of sensor nodes without losing information. The approach is based on an environmental model and the linear dependence of sensor readings. In particular, the spatiotemporal correlation of the sensor node deployment in a large geographic area of interest is minimized. It is also proved that the optimization objective function, which uses the prior estimation error, is submodular and results in a near-optimal solution. Simulation results show that RRLR requires much fewer sensor nodes to achieve the same or lower estimation error when compared to benchmark algorithms.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".