Efficient Green Protocols for Sustainable Wireless Sensor Networks
Bibliographic record
Abstract
Nowadays, wireless sensor networks (WSNs) are widely adopted by many civil/military applications. However, due to the limited capacity of the built-in battery, the lifetime of the sensor is limited, which in turn affects the working time of the whole system. Therefore, the limited energy supply is the most direct and critical constraint to maintain the long-term and efficient operation of the system. Accordingly, reducing energy consumption/improving energy efficiency is an essential prerequisite for designing a sustainable WSN. To address this problem, many approaches have been proposed. To help readers fully understand the techniques/methods in this area of research, we present a taxonomy of the existing energy-efficient strategies for achieving sustainable WSNs. We first introduce some basic concepts and assumptions commonly adopted in energy-efficient WSNs designs. Then, we discuss existing approaches designed for conventional WSNs (consisting of static nodes or nodes with limited mobility) from five aspects: clustering-based schemes, node deployment strategies, node scheduling algorithms, energy-efficient routing schemes, and energy-efficient joint designs. We compare these schemes and highlight their strengths and drawbacks. Additionally, we discuss state-of-the-art approaches relying on some emerging techniques, e.g., high-mobility data collectors, energy-harvesting techniques, etc. Finally, we conclude the paper and present some open challenges.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".