Energy-Efficient Collaborative Offloading in NOMA-Enabled Fog Computing for Internet of Things
Bibliographic record
Abstract
In this work, we investigate the transmission and offloading strategy in the nonorthogonal multiple access (NOMA)-enabled fog computing system for the Internet of Things (IoT). We aim to minimize the total energy consumption of the IoT system while satisfying the latency requirements. Due to the energy minimization problem is a mixed-integer nonlinear programming, we decompose the problem into two subproblems for different optimizing variables, i.e., fog node selection and resource allocation subproblems, and propose a multinode collaboration transmission and computation (MCTC) algorithm. Specifically, the fog node selection subproblem can be transformed into the assignment problem, which is constructed as a bipartite graph to obtain the node selection strategy. For the resource allocation subproblem, we propose an iterative algorithm to obtain the offloading workload, duration allocation, and computation resource. Simulation results are provided, which demonstrate that the proposed algorithm outperforms the other strategies by 56.88% at least.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".