Joint Association, Trajectory, Offloading, and Resource Optimization in Air and Ground Cooperative MEC Systems
Bibliographic record
Abstract
The air and ground cooperative mobile edge computing (MEC) network provides a new paradigm for the development of the Internet of Things (IoT), which enhances the coverage of the terrestrial base station (TBS) and deploys computing resources near IoT devices. In this paper, we construct a UAV-assisted MEC system for IoT networks and design a data processing procedure. The UAV collects data from devices as a relay and makes decisions to offload some tasks to the central server connected with the TBS, while the onboard edge server in the UAV can perform local computing. Furthermore, we jointly optimize the device association, UAV's trajectories, task offloading, and resource allocation to reduce the energy consumption of the entire system. To solve this problem, we decompose it into three tractable sub-problems and use the block coordinate descent (BCD) method to iteratively optimize each set of control variables. Among them, device association is formulated as a linear programming problem, while UAV's trajectory optimization is transformed into a convex problem by introducing slack variables and using successive convex approximation (SCA). The offloading and resource assignment problem is proved to be convex via theoretical analysis and problem transformation. In addition, we derive the optimal relationship between computation duration and computing energy, which greatly reduces the complexity of problems. Simulation results show that the designed system and the algorithms can significantly reduce the total energy consumption, and the offloading strategies have a decisive impact on computation energy consumption.
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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 | 0.001 | 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".