Tree-based modelling of redundancy and paths in wireless sensor networks
Bibliographic record
Abstract
Wireless sensor networks (WSNs) consist of a large number of autonomous nodes randomly deployed in the monitoring area. Nodes, having a short distance from each other, gather information and transmit it to the base station. They are used to monitor a given field of interest. They are widely used for military, environmental, and scientific applications, etc. The performance of wireless sensor networks is greatly influenced by their network topology. Node deployment is a fundamental issue to be solved in wireless sensor networks. In spite of their random deployment, nodes have to organise themselves to avoid redundancy and transceiver tasks. The network has to guarantee complete coverage and connectivity as long as possible. In this paper, we address the problem of network coverage and connectivity and propose an hierarchical model for the wireless sensor networks deployment which consists of dividing sensors in sets of equivalent nodes in order to maintain the connectivity of the network. We study the effectiveness of the model under different deployment strategies: random, circular and Poisson point process distributions. We investigate the impact of deployment strategies on: 1) coverage; 2) connectivity ratio; 3) the shortest path to sink.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".