Efficient Data Harvesting in Urban IoT Networks: DRL for RIS-UAV Communications
Bibliographic record
Abstract
The next generation of wireless communication networks is expected to utilise unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to enhance spectrum and energy efficiency. This work establishes a theoretical foundation for RIS-assisted UAV implementation, capitalising on the passive beamforming capabilities of RIS alongside the adaptable deployment and dynamic mobility of UAVs to enhance internet-of-things (IoT) network performance. A comprehensive framework for RIS-assisted UAV IoT data collection is represented and optimised to enhance critical performance metrics, including the quantity of served IoT devices and achievable data rates. This framework is instrumental in urban IoT networks, such as smart cities, where blockages and fading channels hinder reliable communication. The optimisation strategy deploys a deep reinforcement learning (DRL) algorithm to fine-tune UAV trajectories and IoT device scheduling decisions, complemented by a codebook for RIS beamforming to optimise the RIS phase shift matrix. This integrated approach addresses the ever-increasing demand for efficient data collection in wireless IoT networks, providing a scalable and reliable solution for efficient data collection under dynamic urban environments channel conditions. Simulation results show substantial improvements in system performance, demonstrating the efficiency of the proposed algorithm. By coordinating the RIS phase shift matrix and UAV trajectory planning, the proposed framework achieves improvements in terms of the number of served IoT devices and achievable data rates. For example, compared to baseline methods, our approach outperforms benchmark scenarios by over 50% in terms of the number of served devices. The results reveal the potential of RIS-assisted UAV solutions in meeting the increasing demands of wireless IoT networks.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 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 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".