Consumer-Centric Sustainability: Empowering URLLC in Multi-UAV-Assisted MEC Systems for Industry 5.0
Bibliographic record
Abstract
Consumer-centric energy-efficient 6G networks for Industry 5.0 are essential with the emergence of artificial intelligence and Internet of Things (AIoT) devices, necessitating ultra-reliable low-latency communication (URLLC) services. Moreover, multi unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) systems offer a promising solution to provide URLLC services in dynamic environments. However, they face significant challenges such as communication and trajectory design, high energy consumption, and reliability issues. Our paper introduces a novel approach to address these challenges by developing low-complexity intelligent optimization algorithms based on successive convex approximations (SCA). Our method aims to minimize the weighted sum energy consumption of ground AIoT devices and UAVs while satisfying the quality-of-service (QoS) requirements of URLLC. Simulation results demonstrate the superior performance of our proposed algorithms compared to standard fixed benchmark algorithms, achieving reduced energy consumption and optimizing UAV trajectories. This innovation enhances the sustainability and efficiency of multi-UAV-assisted MEC systems, facilitating the deployment of URLLC services in AIoT environments within consumer-centric energy-efficient 6G networks.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".