Optimal sleep scheduling with transmission range assignment in application-specific wireless sensor networks
Bibliographic record
Abstract
To extend the functional lifetime of battery-operated Wireless Sensor Networks (WSNs), stringent sleep scheduling strategies with communication duty cycles running at sub-1% range are expected to be adopted. Although ultra-low communication duty cycles can cast a detrimental impact on sensing coverage and network connectivity, its effects can be mitigated with adaptive sleep scheduling, node deployment redundancy and multipath routing within the mesh WSN topology. Prior research on this issue had led to the proposal of a new design paradigm called Sense-Sleep Tree (SS-Tree). The SS-Tree aims to harmonise the various engineering issues and provides a method to increase the monitoring coverage and the operational lifetime of mesh-based WSNs engaged in wide-area event-driven surveillance applications. This paper further expands and refines the SS-Tree approach by incorporating practical design considerations such as transmission range assignment, duty cycling and sensing coverage. This approach can achieve higher energy efficiency and satisfy various application requirements during WSN network planning and predeployment phase.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".