Path Planning for Maximizing Area Coverage of Mobile Nodes in Wireless Sensor Networks
Bibliographic record
Abstract
A sensor network consists of tiny, low-powered and multi-functional sensor devices, which can be used to detect and monitor various conditions in the neighborhood of the devices. Regions of the sensing area that are not within the sensing range of any sensor node constitute `coverage holes.' Collecting data from such areas is one of the primary uses of mobile nodes. The path taken by the mobile node(s) can have a significant impact on the performance of the network, regarding the achieved coverage of the sensing area. In this paper, we propose using guided mobility to allow mobile nodes to visit as many coverage holes as possible, within a given time frame. We present a new mixed integer linear program (MILP) formulation that calculates the optimal path to be taken by the mobile node, to maximize the combined total area covered the static and mobile nodes. Simulations with different network sizes and sensing and movement capabilities of the nodes are used 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 | 0.000 |
| Open science | 0.001 | 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".