Demonstration of In-Network Audio Processing for Low-Latency Anomaly Detection in Smart Factories
Huanzhuo Wu, Yunbin Shen, Máté Tömösközi, Giang T.K. Nguyen, Frank H. P. Fitzek · 2022
This demonstration focuses on in-network computing as an enabler for low-latency Industrial Internet of Things (IIoT) applications, such as audio source separation for anomaly detection. By demonstrating a specific industrial application, we show that our method Progressive ICA (pICA), improves accuracy and reduces overall service latency progressively. The idea is to parallelize data transmission and processing along a multi-hop path consisting of in-network computing nodes. The audience can experience the benefits of the novel concept of in-network computing by interacting with the demonstration remotely via the Internet or in person.