Location Uncertainty and Target Coverage in Wireless Sensor Networks Deployment
Bibliographic record
Abstract
In this paper we consider a wireless sensor network (WSN) deployed to monitor a set of targets with known positions. Each target has an associated desired level of coverage by its neighbouring sensor nodes. The network deployment process introduces node placement uncertainty described by known probability distributions. Consequently, deficiency in achieving the desired coverage levels occurs with certain probabilities. To estimate such probabilities, we formalize a target coverage deficiency (TCD) problem. We show that the TCD problem is #P-hard even when restricted to grid WSNs. We then consider networks where node transmission ranges guarantee that the network after deployment has the same connectivity as the planned network. For such networks, we devise a dynamic programming algorithm that can solve a discrete version of the problem exactly and can produce lower bounds on the solution of any arbitrary given instance of the problem. We present simulation results that investigate the accuracy of the algorithm, and illustrate its usefulness in evaluating performance of any given node deployment scheme.
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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 | 0.000 | 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".