A Novel Joint Data Gathering and Wireless Charging Scheme for Sustainable Wireless Sensor Networks
Bibliographic record
Abstract
Energy efficiency is a crucial issue for a practical wireless sensor network (WSN) due to the battery-powered sensors. For improving energy efficiency, many methods have been designed in WSNs thanks to the emerging techniques, i.e., data gathering and wireless charging. The data gathering algorithms are advantageous to decrease the energy consumption of nodes, and the wireless charging schemes can replenish energy to sensors for achieving the semi-permanent WSN. Though lots of approaches of each technique have been designed, the joint methods of both are still lacking. In this paper, a joint data gathering and wireless charging scheme is designed by adopting the mobile chargers (MCs) which can execute the energy charging and the data collection simultaneously. To decrease the data latency, an improved clustering algorithm is implemented first to construct the topology of the WSN. Then, a heuristic-based MC scheduling scheme is proposed for maximizing the charging utility while minimizing the energy consumption of MCs. Compared with the existing joint method, the proposed scheduling scheme achieves the outperformance on delay and charging utility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".