Energy Efficient and Resilient Task Offloading in UAV-Assisted MEC Systems
Bibliographic record
Abstract
Unmanned aerial vehicle (UAV)-assisted Mobile Edge Computing (MEC) presents a critical trade-off between minimizing user equipment (UE) energy consumption and ensuring high task execution reliability, especially for mission-critical applications. While many frameworks focus on either energy efficiency or resiliency, few address both objectives simultaneously with a structured redundancy model. To bridge this gap, this paper proposes a novel reinforcement learning (RL)-based framework that intelligently distributes computational tasks among UAVs and base stations (BSs). We introduce an$(h+1)$-server permutation strategy that redundantly assigns tasks to multiple edge servers, guaranteeing execution continuity even under partial system failures. An RL agent optimizes the offloading process by leveraging network state information to balance energy consumption with system robustness. Extensive simulations demonstrate the superiority of our approach over state-of-the-art benchmarks. Notably, our proposed framework sustains average UE energy levels above 75% under high user densities, exceeds 95% efficiency with more base stations, and maintains over 90% energy retention when 20 or more UAVs are deployed. Even under high computational loads, it preserves more than 50% of UE energy, outperforming all benchmarks by a significant margin—especially for mid-range task sizes where it leads by over 15–20% in energy efficiency. These findings highlight the potential of our framework to support energy-efficient and failure-resilient services for next-generation wireless 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".