A multi-robot sensor-delivery planning strategy for static-sensor networks
Bibliographic record
Abstract
This paper discusses the time-phased deployment of wireless sensor networks, applied to surveillance areas growing in time. The focus herein is on the planning of the time-efficient delivery of static sensors to their designated nodes, given a network configuration. The novelty of the proposed strategy is in that it determines optimal delivery plans for spatio-temporally constrained static-sensor networks using multi-robot teams. The proposed sensor delivery planning strategy starts with an already determined (optimal) network plan specified by sensor placement locations (i.e., nodes) and deployment times. Thus, the goal at hand is to determine the optimal routes for the robots delivering the sensors to their intended locations just-in-time. The travel routes are, thus, determined to maximize spare time for the robots between the nodes. The problem is similar to the multiple travelling salesperson problem, but, with temporal constraints. Namely, sensors must be delivered to their designated nodes at designated (optimized) times in order to maintain the optimal deployment of the network configuration. Furthermore, the strategy is designed to be adaptive to new information that can become available during the search for the mobile target, allowing for replanning of the sensor network (i.e., new sensors locations and new deployment times). Numerous simulated experiments were conducted to validate the proposed 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".