Joint Resource Allocation, Computation Offloading, and Path Planning for UAV Based Hierarchical Fog-Cloud Mobile Systems
Bibliographic record
Abstract
In this paper, the computation offloading problem for the hierarchical fog-cloud computing (FCC) system with unmanned aerial vehicles (UAVs) is studied. The hierarchical FCC, which exploits both centralized and distributed computing architectures, is very promising to support computation offloading in emerging computation-demanding mobile applications. In our design, UAVs integrating computing platforms act as small distributed clouds while the macro base station (BS) integrates a more powerful central cloud server. Furthermore, the multiple input multiple output (MIMO) technology is employed for data communication. We assume that mobile users (UEs) and (UAVs) can change their locations over time and we consider the joint task offloading, user-cloud/cloudlet association, transmit power allocation, and path planning to minimize the total weighted consumed power of the system. To tackle the underlying non-convex mixed integer non-linear program (MINLP), we propose an iterative two-phase algorithm. Specifically, we iteratively solve the user-cloud/cloudlet association problem in the first phase and address the joint resource allocation, path planning problem in the second phase. Furthermore, we employ the difference of convex (DC) optimization method in the second phase to approximate the non-convex bilinear functions and propose to transform the non-convex INLP to the integer linear program (ILP) in the first phase. Numerical studies confirm that the proposed design for the FCC architecture achieves great performance benefits for executing mobile computation tasks.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".