Online Hierarchical Computation Offloading for Marine IoT Networks: A Delay Minimization Approach
Bibliographic record
Abstract
Mobile edge computing (MEC) has emerged as a promising technology for marine Internet of Things (IoT) networks, supporting diverse application requirements that could be both computationally intensive and delay-sensitive. However, most existing studies assume access to pre-existing network information and rely on single-layer MEC frameworks to provide services from an offline perspective, struggling to ensure low latency. To overcome the related issues, we first consider an online hierarchical computation offloading framework in this paper for marine IoT networks with aerial, offshore, and onshore devices. We further develop a hybrid transmission strategy combining non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA) to enhance the computation offloading efficiency within the framework. Considering the time-varying capacity of wireless channels, we thus minimize the hierarchical computation offloading delay by jointly optimizing the offloading strategy and network resource allocation in the marine IoT networks online. To solve the formulated mixed-integer nonlinear programming (MINLP) problem, we design a problem-solving framework based on a decomposition structure. Specifically, we decompose the formulated MINLP problem into two subproblems. For the bottom subproblem, we design a successive convex approximation (SCA)-based algorithm to optimize the hierarchical transmission durations and the offloaded workload with a given user association scheme. For the top subproblem, we propose a deep reinforcement learning (DRL)-based algorithm to realize online optimization of the user association scheme under the time-varying channels. Finally, numerical results demonstrate that the proposed algorithms, including the SCA-based algorithm and the DRL-based algorithm, can reach near-optimal results. Furthermore, the proposed hierarchical computation offloading framework significantly outperforms traditional benchmarks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".