Fuente: PubMed "swarm" Sci Rep. 2026 Apr 18. doi: 10.1038/s41598-026-49107-9. Online ahead of print.ABSTRACTThe rise of connected devices since the industrial revolution has led to vast data generation and new digital challenges. A huge data from smart assets demanded scalable, private, and low-latency solutions. We propose a fog computing approach that brings analytics closer to devices. Our system enhances a standard machine-to-machine architecture using container-based orchestration for autonomy and peer-to-peer cyber-physical system communication. The focus is on smart factories and industrial Internet of Things (IIoT) applications. Recent progress on lightweight deep learning algorithms and fog computing permits multiple model inference tasks to run simultaneously on these resource-limited edge devices, so that we can collaboratively make one thing instead of getting good model quality in each single task. However, the high running latencies overall in multi-model inferences are a drawback for real-time applications. The proposed method introduces a hybrid partial swarm optimization-genetic algorithm scheduler that merges particle swarm optimization and genetic algorithm techniques to fine-tune task initiation times and minimize latency. By leveraging the strengths of both algorithms, it dynamically updates scheduling decisions for enhanced efficiency. This AI-driven model integrates IoT and digital twins to support adaptive, real-time optimization in smart manufacturing environments. Its innovation lies in balancing complex trade-offs across multiple objectives, delivering significant gains in agility and performance within the Industry 4.0 paradigm.PMID:41998135 | DOI:10.1038/s41598-026-49107-9