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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".