Cooperative MEC-Enabled HAPS and UAV-RIS Assisted Task Offloading in ITS Systems
Bibliographic record
Abstract
Non-Terrestrial Networks (NTNs), comprising Unmanned Aerial Vehicles (UAVs) and High Altitude Platform Stations (HAPS) equipped with Mobile Edge Computing (MEC), offer promising solutions for network traffic and tasks offloading from ground users. To enhance the reliability and energy efficiency of such systems, Reconfigurable Intelligent Surfaces (RIS) can be deployed to control wireless signal propagation. In this context, we propose in this paper a novel MEC-enabled framework with HAPS and RIS-equipped UAVs (UAV-RISs) to optimize task offloading from ground users. Our objective is to minimize the tasks' average end-to-end (E2E) delay, under constraints of UAV and HAPS power capacity and E2E service delay threshold, through the optimization of task assignment and UAV-RIS phase-shift configuration. Given the NP-hardness of the problem, we decompose it into two subproblems. The first consists of optimizing the RIS phase shifts to minimize the RIS-assisted communication delay. The second tackles the task assignment problem using a Particle Swarm Optimization (PSO)-based approach, considering the RIS phase shifting solution previously developed. Through simulations, we validate the efficacy of our approach, which outperforms other benchmarks in terms of task average E2E delay and task offloading success rate.
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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 | 0.000 | 0.000 |
| 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".