Federated learning based energy efficient scheme for IoT devices: Wireless power transfer using RIS-assisted underlaying solar powered UAVs
Bibliographic record
Abstract
Devices that are employed in applications related to the Internet of Things (IoT) are constrained by limited energy resources. Consequently, ensuring a continuous supply of energy while also maintaining uninterrupted connectivity within IoT units (IoTUs) is of great importance. In this particular context, we present a scheme that facilitates both, the transfer of wireless power and the transmission of information for IoTUs along with the capability of harvesting solar energy. This scheme is further supported by the utilization of unmanned aerial vehicles (UAV) and the deployment of reconfigurable intelligent surfaces (RIS) for communication purposes. To be more precise, initially, IoTUs obtain energy from the UAV through the process of wireless power transmission (WPT). Subsequently, in the second stage, the UAV retrieves data from the IoTUs using transmitting information. In order to simplify the complexity of the communication issue, we assume that a solar-powered UAV remains stationary at a predetermined altitude. Our objective is to maximize the energy efficiency (EE) of the entire network by coordinating the scheduling of IoTU energy harvesting (EH) and UAV trajectory optimization. We suggest a multi-agent federated reinforcement learning (MFRL) algorithm that maximizes EE through parameter optimization in order to achieve this goal. By utilizing the collective experiences of several agents and reducing energy usage, this algorithm also improves the overall performance of the system. The proposed technique achieves 96.3% and 97.5% accuracy in communication rounds and RIS elements, with a 9−33% increase in EE compared to the best-performing benchmark scheme. The suggested approach outperforms the benchmark algorithms in terms of EE, trajectory optimization, and learning accuracy, according to simulation findings.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".