Optimal Formulation for Maximizing Area Coverage in Wireless Sensor Networks with Mobile Nodes
Bibliographic record
Abstract
Traditional Wireless Sensor Networks (WSN) use stationary sensor nodes to sense relevant events in their vicinity and relay this information for further analysis. These nodes may fail to cover the entire search space either due to inaccurate placement, an insufficient number of nodes or failure of some nodes, resulting in `coverage holes.' Mobile nodes can be deployed, in such cases, to visit the coverage holes and collect the necessary data. It is critical to plan the path taken by these mobile nodes to maximize area coverage and minimize trip time. In this paper, we propose a novel Mixed Integer Linear Programming (MILP) formulation, to determine the path taken by the mobile node. The goal is to find a route that achieves the specified level of coverage in the least amount of time. We have run simulations with different WSN sizes and sensing capabilities of the sensor nodes to evaluate the proposed MILP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".