# In-Network Processing for Low-Latency Industrial Anomaly Detection in Softwarized Networks

arXiv: https://arxiv.org/abs/2112.03683 · HTML: https://mlsanity.com/paper/2112.03683/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [Adaptable and Interpretable Framework for Novelty Detection in Real-Time IoT Systems](https://mlsanity.com/paper/2304.02947/) (2023)

## Abstract (arXiv)

Modern manufacturers are currently undertaking the integration of novel digital technologies - such as 5G-based wireless networks, the Internet of Things (IoT), and cloud computing - to elevate their production process to a brand new level, the level of smart factories. In the setting of a modern smart factory, time-critical applications are increasingly important to facilitate efficient and safe production. However, these applications suffer from delays in data transmission and processing due to the high density of wireless sensors and the large volumes of data that they generate. As the advent of next-generation networks has made network nodes intelligent and capable of handling multiple network functions, the increased computational power of the nodes makes it possible to offload some of the computational overhead. In this paper, we show for the first time our IA-Net-Lite industrial anomaly detection system with the novel capability of in-network data processing. IA-Net-Lite utilizes intelligent network devices to combine data transmission and processing, as well as to progressively filter redundant data in order to optimize service latency. By testing in a practical network emulator, we showed that the proposed approach can reduce the service latency by up to 40%. Moreover, the benefits of our approach could potentially be exploited in other large-volume and artificial intelligence applications.