Tree-based modelling of redundancy and paths in wireless sensor networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 it