Deep Reinforcement Learning for Energy-Efficient Data Dissemination Through UAV Networks
Bibliographic record
Abstract
The unprecedented growth in the number of connected devices has given rise to the Internet-of-Things (IoT) and led to an increasing demand for additional computational and communication resources. Within this context, unmanned aerial vehicles (UAVs) have shown to provide extended coverage, flexibility, and reachability. Motivated by this, in this paper, we develop a UAV-assisted data dissemination framework for IoT networks. To this end, we formulate a joint optimization problem that aims to minimize the total energy expenditure, i.e., the sum of the energy consumed by the UAV and all the spatially-distributed IoT devices. We propose a deep reinforcement learning approach to solve the joint device classification, device association, and path planning optimization problem. In particular, we aim to 1) train a double deep Q-network agent in order to classify devices into two classes, and then using this classification we 2) develop an association algorithm based on the nearest-neighbor heuristic for device association, and 3) develop a path planning algorithm based on the Lin-Kernighan heuristic. Simulation results show that the proposed approach efficiently reduces the energy consumption as compared with the benchmark approaches, i.e., brute force approach and baseline approach. Furthermore, obtained results show that our approach provides a near optimum solution with a fraction of the time required compared to the brute force approach.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".