An approach for bounding breach path detection reliability in wireless sensor networks
Bibliographic record
Abstract
This paper considers wireless sensor networks (WSNs) deployed to provide surveillance against intruders that wish to cross a given area. Due to limited resources, low manufacturing cost, and operation in harsh environments, nodes in such networks are subject to random failure in the field. Hence, there is a need to develop suitable reliability assessment mechanisms to quantify a WSN's ability to perform successfully. Here, we consider one such measure, called the breach path detection reliability (BPDREL), that applies to networks where any intruder crossing a line segment between some adjacent operating pairs of sensor nodes can be detected, and the network perimeter is made of a polygon of such line segments. Each breach path across the network is associated with a pair of entry-exit sides on the perimeter. Our measure takes into account intrusion events associated with any user-specified set of such entry-exit sides. Computing the exact BPDREL can be shown to be #P-hard. We extend existing results on the BPDREL by developing an approach for deriving lower bounds on the problem for arbitrary WSNs where the sink node is located on the network's perimeter. The resulting algorithm is used to analyze the impact of varying various network parameters on the overall network reliability.
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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 | 0.002 | 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 | 0.001 | 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".