Quantifying the effects of placement errors on WSN connectivity in grid-based deployments
Bibliographic record
Abstract
Device deployment plays a key role in the performance of any Wireless Sensor Network (WSN) application. WSN device deployment (i.e. the numbers and positions of the devices) must consider several design factors, like coverage, connectivity, lifetime, etc. However, connectivity remains the most fundamental factor especially in harsh environments. Extensive work has been applied on connectivity in WSN deployments. However, realistic physical deployment errors have been ignored in the majority of that work. In this paper, we explore an efficient grid-based deployment planning for connectivity when sensors placement is affected by random bounded errors around their corresponding grid vertices. We propose a new approach to evaluate the average connectivity percentage of the deployed sensor nodes. We apply this approach to practical 3D deployment scenario, namely, the cubic grid-based deployment with bounded uniform random errors. The average connectivity percentage is computed numerically and verified by extensive simulation results. Based on the results, quantified effects of placement errors on the connectivity percentage are outlined.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".