Toward Energy Efficiency and Fairness in UAV-Based Task Offloading
Bibliographic record
Abstract
The rising demand for compute-intensive mobile applications challenges the limited energy and processing power of user equipment (UE). While Mobile Edge Computing (MEC) enables task offloading to nearby servers, deploying fixed MEC infrastructure is often impractical in settings like disaster zones or temporary high-density events. Furthermore, challenges such as high task delays, limited UE battery life, and unfair load distribution persist. To address these issues, we propose a system where Unmanned Aerial Vehicles (UAVs) serve as mobile relays between UEs and MEC servers. This results in a joint optimization framework combining (i) a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for UAV trajectory control to enhance service coverage and energy efficiency, with (ii) a low-complexity task offloading algorithm for UEs. The framework is explicitly designed to minimize UE energy consumption while promoting fairness in task allocation and data rates. Simulations demonstrate that our approach significantly outperforms state-of-the-art benchmarks, reducing UE energy consumption by 25–30% and improving fairness indices by up to 90%. The proposed system proves scalable and robust, making it suitable for real-time deployment in resource-constrained environments with dynamic workloads.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".