Network Connectivity under Probabilistic Communication Models in Wireless Sensor Networks
Bibliographic record
Abstract
Several previous works have experimentally shown that communication ranges of sensors are not regular disks. Rather, they follow probabilistic models. Yet, many current connectivity maintenance protocols assume the disk communication model for convenience and ease of analysis. In addition, current protocols do not provide any assessment of the quality of communication between nodes. In this paper, we take a first step in designing connectivity maintenance protocols for more realistic communication models. We propose a distributed connectivity maintenance protocol that explicitly accounts for the probabilistic nature of communication links and achieves a given target communication quality between nodes. Our protocol is simple to implement, and we demonstrate its robustness against random node failures, inaccuracy of node locations, and imperfect time synchronization of nodes using extensive simulations. We compare our protocol against others in the literature and show that it activates fewer number of nodes, consumes much less energy, and significantly prolongs the network lifetime.
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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 | 0.001 |
| 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".