LRSA: A multi-component Wireless Sensor Network management framework
Bibliographic record
Abstract
Although Wireless Sensor Networks (WSNs) have significant applications in monitoring, security and other areas, they still lack network management solutions to enable large scale adoption. Such solutions would help in determining the degree of data aggregation prior to transforming it into useful information, localizing the sensors accurately, scheduling and routing data by reducing end-to-end delay, and energy consumptions. Moreover, to the best of our knowledge, no integrated network management framework consisting of efficient localization, data scheduling, routing, and data aggregation approaches exists in the literature for a large scale WSN. Thus, we introduce an integrated management framework comprising sensors Localization, Routing, data Scheduling, and Aggregation (LRSA) for a large scale WSN. Simulation results show that LRSA outperforms other approaches in terms of localization energy consumptions and error, end-to-end delay, and network energy consumptions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".