ENERGY EFFICIENT DATA DISSEMINATION FOR UNIFORM COVERAGE 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) are commonly used for continuous monitoring applications. However, due to remote and inaccessible WSN deployments, the batteries cannot be easily replaced. As a result, the quality of sensor monitoring changes with respect to time. We investigate the rate of change in coverage as sensor nodes are depleted of their energy resources. In this paper, we propose a coverage analysis method which not only focuses on the coverage itself but also on its uniformity and efficiency. The paper documents interesting network coverage evaluation studies showing several ways of getting valuable information about a WSN's coverage's uniformity and efficiency by appropriately interpreting the change in its efficient and redundant coverage ratios (efficient and redundant coverage ratios are terms defined in this paper). We also specify the particular WSN applications where the proposed coverage analysis method will be more suitable.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it