Resource optimization for minimizing latency and cost in UAV-assisted mobile edge computing (MEC) networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) enable a mobile edge computing (MEC) paradigm with reduced latency by bringing computational resources closer to the network edge. However, UAV-MEC servers have less computation and caching resources than ground base stations (BSs). The management of communication and control resources is crucial to coordinate communication, computing, and caching due to the involvement of aerial networks. Thus, managing joint caching, communication, computing, and control (4C) resources is vital in UAV-assisted MEC networks. To address these challenges, we developed a computational model for efficient resource management to reduce the linear combination of network cost and latency under constrained caching, computing, and offloading. We used binary decision variables for the allocation of computational and offloading resources. The formulated problem is a binary linear programming problem incorporating binary decision variables and linear constraints. We propose an interior point method-based heuristic to obtain a sub-optimal solution with low complexity. Simulation results demonstrate the effectiveness of our proposed approach compared to the branch and bound algorithm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it