Industry 4.0 for smart transport systems: Foundations and applications
Bibliographic record
Abstract
• Industry 4.0 enables energy-aware transport via CPS, IIoT, and edge analytics. • Foundations mapped to V2G, predictive maintenance, and optimization tasks. • Secure interoperability and data pipelines support scalable deployments. • Edge and cloud AI enable real-time decisions under grid and mobility limits. • Industry 4.0 foundations pave the road toward human-centric Industry 5.0. This paper examines how Industry 4.0 technologies enable smart transport systems through energy-aware architectures that integrate vehicles with the smart grid. We provide a concise synthesis of application patterns across cyber-physical systems (CPS), Industrial IoT sensing, edge and cloud analytics, and secure data exchange, and present application-oriented cases spanning EV–grid interaction (V2G), predictive maintenance, and operational optimization. We map enabling components—data ingestion, model inference, decision support, and secure interoperability—to transport tasks and discuss implementation trade-offs observed in practice. While our analysis is grounded in Industry 4.0 foundations, we explain how these foundations support a measured transition toward Industry 5.0—prioritizing human-centric, resilient, and sustainability-aligned operations— with Industry 5.0 features and LLM-based interfaces treated as future work rather than scope-defining elements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".