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

TL;DR

This paper introduces IA-Net-Lite, an in-network processing system for industrial anomaly detection that reduces latency by up to 40% by leveraging intelligent network devices to process and filter data directly within the network.

Contribution

The paper presents the first in-network data processing system for industrial anomaly detection, enhancing latency performance in smart factory networks.

Findings

Reduced service latency by up to 40% in tests

Demonstrated effectiveness of in-network processing for AI applications

Potential applicability to other large-volume data scenarios

Abstract

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.