Joint Online Optimization of Data Sampling Rate and Preprocessing Mode for Edge–Cloud Collaboration-Enabled Industrial IoT
Bibliographic record
Abstract
Edge–cloud collaboration is critical in the Industrial Internet of Things (IIoT) for serving computation-intensive tasks (e.g., bearing fault monitoring) that require low-response delay, low energy consumption, and high processing accuracy. In this article, an energy-efficient resource management framework for IIoT with closed-loop control on end devices, edge servers, and cloud center is studied. In the considered model, each edge server aggregates the data collected by industrial sensors (i.e., end devices) and forms computation tasks for corresponding data analysis. In order to minimize the system-wide energy consumption, while maintaining a guaranteed service delay and a satisfied data processing accuracy for each IIoT application, a joint optimization of: 1) sensors’ sampling rate adaption; 2) edge servers’ preprocessing mode selection; and 3) edge–cloud communication and computing resource allocation is formulated. Further taking into account the time-varying channel conditions and randomness of data arrivals, we propose a low-complexity online algorithm, which solves the problem in a dynamic manner. Particularly, the Lyapunov optimization method is first utilized to decompose the long-term problem into a series of instant ones [mixed-integer nonlinear programming (MINLP) problems], and then a Markov approximation algorithm is applied to solve such instant problems to near optimum with the consideration of future impacts. Performance analyses and simulation results show that the proposed algorithm is feasible under long-term service satisfaction constraints, and its energy consumption and service delay are approximately 20% and 28% lower than those of the benchmark schemes, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".