# Positioning Fog Computing for Smart Manufacturing

arXiv: https://arxiv.org/abs/2205.10860 · HTML: https://mlsanity.com/paper/2205.10860/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 22 May 2022.

## Builds on (most cited first, 4 of 4)

- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [Latency and Throughput Characterization of Convolutional Neural Networks for Mobile Computer Vision](https://mlsanity.com/paper/1803.09492/) (2018)
- [Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks](https://mlsanity.com/paper/2202.10541/) (2022)
- [YASMIN: a Real-time Middleware for COTS Heterogeneous Platforms](https://mlsanity.com/paper/2108.00730/) (2021)

## Abstract (arXiv)

We study machine learning systems for real-time industrial quality control. In many factory systems, production processes must be continuously controlled in order to maintain product quality. Especially challenging are the systems that must balance in real-time between stringent resource consumption constraints and the risk of defective end-product. There is a need for automated quality control systems as human control is tedious and error-prone. We see machine learning as a viable choice for developing automated quality control systems, but integrating such system with existing factory automation remains a challenge. In this paper we propose introducing a new fog computing layer to the standard hierarchy of automation control to meet the needs of machine learning driven quality control.