A Novel Joint Optimization Method Based on Mobile Data Collection for Wireless Rechargeable Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
In wireless sensor networks, energy efficiency is a very critical issue as it impacts the network's lifetime and performance. Mobile data collection and wireless charging are two promising emerging techniques for enhancing energy efficiency. To achieve high charging rates and implement data collection with less energy consumption of sensors, we design a joint data collection and energy charging scheme by taking the strengths of these two techniques. The mobile charger is able to implement the energy charging and data collection simultaneously when it is equipped with the appropriate communication and charging hardware. We provide a two-step method for this joint problem. First, the topology is constructed by a novel clustering algorithm that aims to balance the number of clusters and the energy consumption of inter-cluster communication. Second, two modes of scheduling schemes are developed for facing the scenarios with different delay requirements (i.e., delay-tolerant scenario and delay-aware scenario) with heuristic algorithms. Compared with existing state-of-the-art methods, the simulation results present that our proposed scheduling schemes achieve the outperformance on packet delay and charging efficiency.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it