Bibliographic record
Abstract
Wireless sensor networks (WSNs) have many applications in industry and environmental monitoring where sensor nodes are deployed at fixed places for monitoring some phenomena. One of the commonly used deterministic deployment topologies is a rectangular grid. In a WSN reliability measure that considers the aggregate flow of sensor data into a sink node is formulated, and it has been shown that computing this measure for an arbitrary WSN is #P-hard. Thus, it is unlikely that efficient algorithms for solving the problem exist. In this paper we consider a WSN deployed on rectangular W times L grid (WSG) and show that the problem remains #P-hard even when restricted to the grid graph model. We then present a routing scheme upon which we develop an O(nL2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</sup> ) algorithm to compute the exact WSG reliability. Therefore, for W << L (thin grid or strip area) the algorithm is polynomial in n, while for a rectangle with arbitrary dimensions the running time is O(nradicn2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">radicn</sup> ). We also present numerical results that demonstrate some of the potential applications of the algorithm. A noteworthy finding is that significant improvement in the WSG reliability can be achieved using more reliable sensors at the two boundaries adjacent to the sink node.
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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".