An Energy-efficient UAV-based Data Aggregation Protocol in Wireless Sensor Networks
Bibliographic record
Abstract
Energy efficiency is an important issue in Wireless Sensor Networks (WSNs). The energy is mainly consumed by data sensing, data transmission and movement of sensors. The energy consumed by data transmission is much larger than data sensing. A potential solution for energy saving is applying the external devices to collect data to prolong the network lifetime. In this paper, we adopt an unmanned aerial vehicle (UAV) serving as the data mule and propose a novel energy-efficient UAV-based data aggregation protocol in WSNs to reduce the energy consumption of sensors. By considering a clustered WSN, our approach computes an optimal path for data mule through all cluster heads (CHs) while achieving a relatively high system-wide energy efficiency. Moreover, we introduce a genetic algorithm to derive a near-optimal solution for a large-scale WSN while reducing the computing time. We compare our protocol with state-of-the-art approaches, and the simulation results demonstrate that the proposed algorithm improves the network performance on energy efficiency.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".