UAV-Assisted IoT Data Collection Scheme for IRS-Enabled Backscatter Communication Networks
Bibliographic record
Abstract
Data collection schemes assisted by uncrewed aerial vehicle (UAV) play a key role in Internet-of-Things (IoT) networks. Nonetheless, the massive IoT devices bring new challenges, especially the high energy consumption. The backscatter communication (BackCom) technique can utilize ambient signals for passive transmission, thus being applicable to IoT systems. Due to its numerous reflecting elements, Intelligent Reflecting Surface (IRS) can accomplish backscatter with high beamforming gain. Thus, IRS is suitable to alleviate the double-fading effect, which significantly restrains the development of BackCom networks. This paper utilizes the IRS-based BackCom technique to achieve efficient IoT data collection with the help of a UAV. The UAV provides carriers for multiple IRSs, which can emit data through backscatter. Then, the UAV collects the backward data dynamically. To maximize the collected data, the UAV’s transmit power, trajectory, and the IRS’s beamformers are jointly optimized. The optimization is realized through an alternative algorithm based on block coordinate descent (BCD) and reinforcement learning (RL). The Lagrangian dual transformation and semi-definite relaxation (SDR) approaches are employed to address the non-convexity of the problem effectively. Finally, simulation results demonstrated the effectiveness and feasibility of the proposed optimization scheme in the introduced system model.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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".