Collaborative Service Provisioning in IIoT Systems via Service Urgency and Situation-Adaptive Goal Modeling: A Dynamic Service–Energy Tradeoff
Bibliographic record
Abstract
Efficient use of scarce communication and computing resources is critical for meeting the diverse requirements of vertical industrial applications in the Industrial Internet of Things (IIoT). However, the randomness of concurrent service arrivals and the diversity of service demands result in dynamic competition for limited resources at a specific time, presenting significant challenges for effective service provisioning in IIoT systems with changing conditions. To address this challenge, this article investigates a collaborative service provisioning scheme that incorporates service urgency and situation-adaptive system goals to meet diverse vertical applications. Specifically, a novel concept, urgency of service, is introduced to characterize the sensitivity of service to limited resources, thereby mitigating resource competition by prioritizing concurrent services. To cope with system uncertainty, we design a situation-adaptive system goal that enables a dynamic tradeoff between service demands and system energy consumption. For this purpose, we develop a comprehensive metric that integrates the Value of Service (VoS) and system energy requirements, termed VoSE, to customize the time-varying system goals. The overall goal is to maximize the long-term VoSE, which is formulated as a mixed-integer nonlinear programming (MINLP) problem. Since it is NP-hard, we decompose it into a service provisioning subproblem (SPP) and a dynamic system goal subproblem (DGP). A reverse auction-based and urgency-driven service provisioning algorithm is first developed to solve the SPP. Furthermore, a dynamic system operation goal determination algorithm based on the VoSE ratio is proposed for the DGP. Extensive simulation results validate the effectiveness of the proposed algorithm in various performance parameters and demonstrate its significant superiority over baseline schemes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".