Resource Scheduling and Delay Optimization of IoT Devices in Drone-Assisted Multiaccess Edge Computing
Bibliographic record
Abstract
Multiaccess edge computing (MEC) plays a crucial role in providing low-latency and high-data transmission services to Internet of Things (IoT) devices. However, in remote areas where deploying edge devices is challenging, optimizing delay remains a significant research focus. To address this issue, our research investigates a multidrones-assisted IoT task offloading model. In this model, tasks generated by IoT devices equipped with energy harvesting (EH) capabilities are offloaded to MEC servers with the assistance of multiple drones. In order to monitor and manage the energy consumption of IoT devices and the task backlog of edge servers, the energy consumption and task update queues are established. We formulate a mixed integer nonlinear programming (MINLP) problem, which aims to optimize the allocation of communication and computation resources to minimize the execution latency of IoT devices. To ensure the stability of each queue, we employ the weighted perturbation method within the Lyapunov optimization framework to decompose the original problem. And a low complexity multidrones assisted offloading (MUAO) algorithm is designed. Simulation results show that MUAO consistently exhibits lower latency and energy consumption compared to the baseline scheme and other existing algorithms, while maintaining a low packet loss rate of only 5%.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".