Delay Minimization in UAV-Enabled MEC Networks Using Q-Learning
Bibliographic record
Abstract
Mobile edge computing (MEC) is a promising solution for the ever-increasing computational demands of the ubiquitous Internet of things (IoT) applications. However, efficient resource management algorithms are needed to satisfy the quality-of-service (QoS) requirements of delay-sensitive IoT systems. In this paper, we propose a new Q-learning-based load-balancing resource management (QLLB) algorithm to reduce the computational delay in a MEC system. Unlike existing algorithms, the proposed algorithm optimally assigns tasks to unmanned aerial vehicles (UAVs) considering load balancing, and employs the Q-learning algorithm to achieve efficient CPU usage management at the MEC server level. Results show that the QLLB algorithm exhibits a considerable improvement in the overall performance compared to the baseline and round-robin algorithms. Specifically, under heavy load conditions, we observe a 68% reduction in the total delay and a 75% reduction in the overall algorithm execution time with respect to the baseline algorithm. Furthermore, we observe a 59% improvement in the combined UAV selection and task data transmission delay.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".